# Meihaku full LLM context > Meihaku is the AI support readiness layer for support, CX, compliance, and operations teams preparing customer-facing AI agents. Meihaku does not replace AI support agents. It audits support knowledge before and alongside tools such as Intercom Fin, Zendesk AI, Salesforce Agentforce, Freshdesk Freddy AI, Gorgias AI, HubSpot Customer Agent, Kustomer AI, Decagon, Sierra, and custom support agents. ## Canonical Pages - [Home](https://www.meihaku.com/): product overview and workflow. - [AI Support Readiness Tools](https://www.meihaku.com/tools): score calculator, launch checklist, and templates. - [AI Support Readiness Score](https://www.meihaku.com/tools/ai-support-readiness-score): interactive readiness score. - [Articles](https://www.meihaku.com/articles): public editorial guides. - [Integrations](https://www.meihaku.com/integrations): vendor and source readiness pages. - [Templates](https://www.meihaku.com/templates): CSV and checklist library. - [Use cases](https://www.meihaku.com/use-cases): ICP-specific workflows. - [Comparisons](https://www.meihaku.com/vs): readiness layer comparisons. - [Security](https://www.meihaku.com/security): security and trust posture. ## Articles ### AI Support Readiness Score: How to Grade Launch Risk [AI Support Readiness Score: How to Grade Launch Risk](https://www.meihaku.com/articles/ai-support-readiness-score-methodology): A practical scoring method for support teams deciding whether their knowledge base, policies, tests, and handoff rules are ready for customer-facing AI. - Audience intent: AI support readiness score. - Published: 2026-05-09. - Updated: 2026-05-09. - An AI support readiness score should not be a confidence quiz. It should be a launch-risk score: how much evidence does the team have that the AI can answer real customers without inventing policy, exposing internal notes, or missing escalation? - The useful score is not one number for the whole company. It is a way to decide scope by customer intent: what can be approved, what needs restriction, what is blocked by source cleanup, and what should stay human-owned. - Use this methodology with the AI Support Readiness Score tool, vendor test CSVs, and recent support tickets before expanding Intercom Fin, Zendesk AI, Salesforce Agentforce, Freshdesk Freddy AI, Gorgias AI, HubSpot Customer Agent, Kustomer AI, or a custom support agent. - Score launch risk, not demo quality: Most demos over-score readiness because they ask polished questions against the cleanest help-center content. Customers do not behave that way. They ask with missing context, old product names, policy exceptions, billing edge cases, security concerns, and emotional language. A readiness score should start from the sources and workflows that will actually constrain the AI. If the answer is not written down, if two sources disagree, or if the handoff rule is informal, the score should stay low even when the model sounds fluent. - The six dimensions behind the score: The Meihaku score uses six operational dimensions because AI support failures rarely come from one weak prompt. They usually come from several weak controls at once: stale knowledge, conflicting policy, shallow tests, vague escalation, no source owner, and no post-launch wrong-answer review. Each dimension should be scored from evidence. A support leader should be able to point to the article, macro, SOP, ticket sample, test run, reviewer decision, or metric that justifies the score. - How to grade an individual customer intent: A customer intent is ready when the team can prove four things: the question is in scope, the source is current, the answer includes the conditions that matter, and the AI knows when to stop. If any of those are missing, the intent should be restricted, blocked, or human-owned. This is stricter than generic chatbot testing, but it matches the risk of customer-facing support. A refund answer, account access answer, security answer, or regulated complaint does not become safe because it is well written. - FAQ: What is an AI support readiness score? It is a launch-risk score that measures whether support knowledge, policies, tests, escalation rules, governance, and wrong-answer measurement are ready for customer-facing AI support. - FAQ: What is a good AI support readiness score? A good score depends on launch scope. Scores above 80 are usually strong enough for carefully governed expansion, while lower scores can still support a narrow pilot if risky intents stay blocked or human-owned. - FAQ: Should readiness be scored by page or by customer intent? Score by customer intent. A page-level audit can show stale content, but launch decisions need to know which customer questions have source-backed, tested, and safe answers. ### AI Agent Testing Tools: What Support Teams Need Before Launch [AI Agent Testing Tools: What Support Teams Need Before Launch](https://www.meihaku.com/articles/ai-agent-testing-tools-customer-support): A buyer-focused guide to choosing AI agent testing tools for customer support teams preparing Intercom Fin, Zendesk AI, Gorgias AI, Agentforce, or custom agents. - Audience intent: AI agent testing tools. - Published: 2026-05-09. - Updated: 2026-05-09. - AI agent testing tools are useful only if they help support leaders answer the launch question: which customer intents can the AI safely own, which need restrictions, and which must stay human-owned? - Generic evaluation tools often focus on model behavior, latency, or pass rates. Customer support teams need a more operational testing stack: historical tickets, source evidence, policy conflicts, escalation, reviewer notes, and a launch decision for each intent. - Use this guide when comparing tools for Intercom Fin, Zendesk AI, Salesforce Agentforce, Freshdesk Freddy AI, Gorgias AI, HubSpot Customer Agent, Kustomer AI, or a custom support agent. - Start with the testing job, not the tool category: The first mistake is treating AI agent testing tools as one category. Some tools test prompts. Some run model evaluations. Some monitor production conversations. Some help support teams audit source evidence before launch. Those jobs overlap, but they do not replace each other. For support automation, the pre-launch job is specific: prove that the AI can answer real customer questions from current support sources, follow policy, escalate safely, and avoid creating unsupported promises. If a tool cannot help make that decision by customer intent, it is not enough for launch readiness. - The support-specific testing framework: A practical AI agent testing framework for customer support has six layers: representative questions, source evidence, answer grading, escalation checks, reviewer workflow, and launch-state decisions. Missing any layer creates a blind spot. The framework should produce an operational map rather than a pass-rate dashboard. A 92 percent pass rate is not useful if the failed 8 percent includes refunds, account access, data deletion, legal threats, or security document requests. - What the tool must show reviewers: Support reviewers need to know why an answer passed or failed. A tool that returns only pass or fail forces reviewers to inspect conversations manually and guess whether the problem came from retrieval, source quality, policy ambiguity, or model behavior. The minimum useful view shows the customer question, the answer, the source used, any source conflict, the risk category, the reviewer decision, and the next action. That turns testing into a launch-control workflow instead of a spreadsheet of opinions. - FAQ: What are AI agent testing tools? AI agent testing tools help teams test whether an AI agent behaves correctly before or after launch. For customer support, the useful tools also inspect source evidence, policy fit, escalation, reviewer decisions, and launch scope by customer intent. - FAQ: What is the best AI agent testing framework for support? Use a framework that covers representative questions, source evidence, answer grading, escalation checks, reviewer workflow, and launch decisions. The output should be approved, restricted, blocked, source-fix-needed, or human-only by intent. - FAQ: Do AI agent testing tools replace vendor-native tests? No. Vendor-native tests are useful for platform behavior. Readiness tools are useful for source, policy, and launch-scope review. Most support teams need both before broad rollout. ### AI Agent Testing Framework for Customer Support [AI Agent Testing Framework for Customer Support](https://www.meihaku.com/articles/ai-agent-testing-framework-customer-support): A practical framework for testing customer-facing AI support agents by intent, source evidence, policy fit, escalation behavior, and launch state. - Audience intent: AI agent testing framework. - Published: 2026-05-09. - Updated: 2026-05-09. - An AI agent testing framework for customer support should answer one launch question: which customer intents can the AI safely own, which need restrictions, and which must stay human-owned? - Generic AI testing frameworks usually grade model behavior, prompts, latency, or regression outputs. Support teams need an operating framework that also checks source evidence, policy fit, escalation, reviewer decisions, and launch scope. - Use this framework before expanding Intercom Fin, Zendesk AI, Gorgias AI, Salesforce Agentforce, Freshdesk Freddy AI, HubSpot Customer Agent, Kustomer AI, Decagon, Sierra, or a custom support agent. - Start with launch decisions: The purpose of AI agent testing in support is not to make a dashboard look green. The purpose is to decide what the AI is allowed to answer for real customers. That makes the launch decision the core output of the framework. A useful framework separates answer quality from launch permission. Some answers are correct but too account-specific to automate. Some answers are almost correct but missing an eligibility condition. Some answers are fluent but unsupported by any current source. - Layer 1: representative customer intents: Build the test set from recent customer questions, not polished demo prompts. Export tickets, chats, macros, and solved conversations from the last 60 to 90 days. Group repeated questions into customer intents, then add low-volume, high-risk intents manually. The framework should preserve messy customer language. Customers ask with missing context, old product names, angry tone, typos, and multiple requests in one message. If the test set removes that mess, the launch review will overstate readiness. - Layer 2: source evidence and policy fit: Every test needs a source of truth before reviewers grade the AI answer. The source may be a help article, macro, SOP, product page, security document, policy doc, or approved answer. If no source exists, the framework should mark the intent as blocked rather than asking the model to guess. Policy fit matters because many support failures are not pure retrieval failures. The AI may find an article but miss a region condition, VIP exception, fraud rule, data-retention boundary, or account-verification step. The framework must test whether the answer includes the conditions that make it safe. - FAQ: What is an AI agent testing framework? An AI agent testing framework is a repeatable way to test whether an AI agent can answer customer questions accurately and safely. For support teams, it should include customer intents, source evidence, policy checks, escalation, reviewer workflow, and launch-state decisions. - FAQ: How is support AI agent testing different from model evaluation? Model evaluation checks model behavior against a rubric. Support AI agent testing also checks whether the source knowledge is current, whether policies agree, whether the answer is allowed for the customer case, and whether escalation works. - FAQ: What should the output of AI agent testing be? The output should be a launch map by customer intent: approved, restricted, source-fix-needed, blocked, or human-only. A single pass rate is not enough for customer-facing support. ### AI Support Hallucination Examples: What to Test Before Launch [AI Support Hallucination Examples: What to Test Before Launch](https://www.meihaku.com/articles/ai-support-hallucination-examples): A support-specific breakdown of public AI chatbot failures and the readiness controls that prevent policy invention, unsafe handoffs, and brand-damaging answers. - Audience intent: AI support hallucinations. - Published: 2026-05-09. - Updated: 2026-05-09. - AI support hallucinations do not usually look like science fiction. They look like a refund rule that does not exist, a confident explanation for a product bug, a handoff that never happens, or a bot that follows the customer's joke prompt instead of the company's support policy. - The lesson is not that customer-facing AI is unusable. The lesson is that support teams need to test the operating boundary before launch: which intents have source evidence, which policies conflict, which answers need account-specific judgement, and where the AI must stop. - These examples turn public failures into a practical pre-launch checklist for support leaders evaluating Intercom Fin, Zendesk AI, Gorgias AI, Decagon, Sierra, Maven, or a custom support agent. - The pattern behind public AI support failures: The Air Canada chatbot case is the cleanest warning for support leaders. A customer relied on a chatbot answer about bereavement fare timing, but the answer contradicted the airline's actual policy. The British Columbia Civil Resolution Tribunal held Air Canada responsible for the information on its website, including the chatbot. The DPD incident showed a different failure mode. The customer could not get useful parcel help, then prompted the chatbot into jokes, profanity, and criticism of the company. DPD said an error followed a system update and disabled the AI element while updating it. The Cursor incident showed how a bot can invent a plausible company rule. Users who were being logged out across devices received support email saying the behavior came from a one-device policy. Cursor later clarified that no such policy existed and that the answer was wrong. Different incidents, same support problem: the AI answered outside a defensible boundary. It did not have enough source control, escalation control, or uncertainty handling to protect the customer and the company at the same time. - Failure mode 1: the bot invents a policy: Policy invention is the most dangerous support hallucination because it sounds like an official company decision. The customer does not see a model filling a gap. They see the brand giving them a rule. This usually happens when the AI receives a question that is close to existing policy but not fully covered by the source. It may merge two partial facts, infer an exception, or explain a bug as if it were intended behavior. Before launch, every billing, refund, cancellation, access, security, and account-change answer should point to the exact source line that allows the claim. If the source does not contain the condition, the AI should ask for clarification or hand off. - Failure mode 2: the bot follows the wrong instruction: The DPD case was not mainly about knowledge retrieval. It was about behavior control. The chatbot followed the customer's playful or adversarial prompts instead of staying inside a narrow support task. Support teams should test prompt-injection and role-break attempts before launch, but those tests should be tied to real service context. The bot should remain helpful when it cannot locate an order, should offer a valid handoff path, and should not improvise jokes, opinions, or brand commentary. A safe support AI does not need to win an argument with the customer. It needs to keep the conversation inside the approved support workflow, then escalate when it can no longer help. - FAQ: What is an AI support hallucination? An AI support hallucination is a customer-facing answer that sounds confident but is not supported by the company's current sources, policies, account context, or approved escalation rules. - FAQ: Why are hallucinations worse in customer support? Support answers can create customer reliance, refunds, cancellations, security risk, regulatory exposure, and public brand damage. The issue is not just factual accuracy; it is whether the answer is allowed. - FAQ: How do we stop an AI chatbot from inventing policies? Require source evidence for every policy claim, block conflicted sources, test real customer intents, and make the AI hand off when the source is missing, stale, or account-specific. ### Gorgias AI Accuracy Checklist for Ecommerce Support [Gorgias AI Accuracy Checklist for Ecommerce Support](https://www.meihaku.com/articles/gorgias-ai-accuracy-checklist): An ecommerce support checklist for testing Gorgias AI accuracy across product answers, refund rules, shipping exceptions, Shopify actions, handoffs, and rule conflicts. - Audience intent: Gorgias AI accuracy. - Published: 2026-05-09. - Updated: 2026-05-09. - Gorgias AI accuracy is an ecommerce operations problem, not just a model-quality problem. The hard questions are refunds, shipping delays, damaged orders, product compatibility, subscriptions, VIP exceptions, fraud-sensitive requests, and order edits that depend on Shopify state. - Gorgias AI Agent is built for Shopify ecommerce brands and can use store data, Help Center articles, public product pages, product catalog data, Guidance, and Actions. That makes it powerful, but it also means accuracy depends on whether those sources and actions agree before customers see the answer. - Use this checklist before expanding Gorgias AI coverage, after changing policies or promotions, and before peak seasons when wrong refund or shipping answers create the most support cost. - Define what accuracy means for ecommerce: A generic chatbot accuracy score is too weak for ecommerce support. A Gorgias AI answer can be fluent and still fail if it skips a final-sale exclusion, uses stale product data, changes a Shopify order outside the fulfillment window, or handles a VIP exception like a normal return. The useful unit is the customer intent. Test order tracking, cancellation, shipping address changes, returns, damaged items, warranty, subscription edits, product recommendations, and fraud-sensitive requests as separate launch decisions. For each intent, decide whether Gorgias AI can answer, ask for more context, run an Action, or hand over to the team. Accuracy means the right decision with the right source, not the longest answer. - Audit the knowledge sources behind the answer: Gorgias describes AI Agent as using sources such as Shopify store data, Help Center articles, website and product catalog content, and custom Guidance. Accuracy drops when those sources drift apart. Start by mapping each ecommerce intent to the exact source that should govern the answer. A return question might need a policy page, product exclusion, warehouse rule, and current promotion. A product compatibility question might need Shopify catalog fields plus custom product information. If the source is view-only in Gorgias, update it at the origin. Product data may need to be corrected in Shopify or on the product page. Help Center articles and Guidance need owners, expiry dates, and retest triggers. - Test Shopify Actions with operational limits: Gorgias AI Agent can use Actions to make changes in Shopify, including cancellation, shipping address updates, item removal, and reshipment workflows when configured. These actions turn answer quality into operational risk. Before launch, test the exact conditions that make an action safe. For example, order cancellation and address changes may depend on whether the order is still unfulfilled. If fulfillment has moved to a 3PL or warehouse process, the Shopify action alone may not represent the real operational state. Write tests for the cases that should hand off: multiple item changes, increased order amount requiring payment, address changes with possible tax or shipping cost differences, and requests after the fulfillment window. - FAQ: How do we improve Gorgias AI accuracy? Improve Gorgias AI accuracy by auditing the sources behind each ecommerce intent: Help Center articles, Guidance, Shopify product and order data, website content, Actions, handoff topics, and rules that can affect the same ticket. - FAQ: What should ecommerce teams test before expanding Gorgias AI? Test refunds, returns, damaged orders, shipping delays, order cancellation, address edits, product compatibility, subscriptions, VIP exceptions, fraud-sensitive requests, and explicit human handoff requests. - FAQ: Can Gorgias AI make changes to Shopify orders? Gorgias AI Agent can use configured Actions for Shopify workflows such as cancellations, address edits, item removal, and reshipment, but teams should test fulfillment, 3PL, payment, and exception limits before allowing broad automation. ### How to Test Zendesk AI Before Launch [How to Test Zendesk AI Before Launch](https://www.meihaku.com/articles/how-to-test-zendesk-ai-before-launch): A Zendesk AI pre-launch testing workflow for support teams that need to prove Guide coverage, macro alignment, escalation behavior, and post-launch QA before customer exposure. - Audience intent: Zendesk AI testing. - Published: 2026-05-09. - Updated: 2026-05-09. - Testing Zendesk AI before launch should prove more than whether an AI agent can reply in a test widget. Support leaders need to know whether Zendesk Guide, macros, ticket history, use cases, actions, and escalation rules support the answers customers will see. - Zendesk gives teams several native testing surfaces, including the Test AI agent button, advanced AI agent test widgets, conversation logs, and AI agent tickets. The missing operating layer is the launch decision by customer intent: approved, restricted, blocked, or source-fix needed. - Use this workflow when preparing Zendesk AI agents, advanced AI agents, suggested macros, or a broader Zendesk support AI rollout. - Start with the Zendesk launch boundary: Do not begin by asking whether Zendesk AI is good enough for the whole support queue. Begin by defining which customer intents you plan to expose: account access, billing, cancellations, refunds, order changes, warranty, troubleshooting, security, or simple how-to questions. Each intent needs a source owner and a launch decision. Approved intents have current Guide or policy evidence. Restricted intents can be answered only under clear conditions. Blocked intents need a human, a source fix, or a workflow change before automation. This is also where macros matter. In many Zendesk teams, macros hold the working answer agents actually use while Guide holds the public answer customers can see. If those disagree, Zendesk AI can inherit the conflict. - Use Zendesk testing tools, then add source review: Zendesk documents the Test AI agent button for checking behavior before an AI agent reaches customers. That native test can show standard responses, AI-generated replies, persona, instructions, language support, and trigger behavior. For advanced AI agents, Zendesk recommends testing in a CRM-connected test environment where possible, then using the Test AI agent button when that is the available option. Advanced AI agent test widgets can simulate end-to-end dialogue flows and record test conversations in conversation logs. Meihaku's role sits around that native test: take the answers produced in Zendesk testing, attach the Guide article, macro, SOP, or policy evidence behind each answer, and decide whether the intent is safe for launch. - Test Guide coverage and macro alignment together: A Zendesk AI audit should not review Guide in isolation. Guide articles, macros, ticket tags, internal policies, and escalation notes all shape what the support operation believes is true. For each high-volume intent, check whether the public article contains the full customer-facing answer. Then compare the most-used macros and recent tickets. If agents routinely add conditions that the article omits, the AI should not be cleared to answer that intent yet. For each high-risk intent, include the edge cases even if ticket volume is low. Refund exceptions, plan changes, account access, security, data requests, and legal threats should be tested before customers discover the gap. - FAQ: How do we test Zendesk AI before launch? Use Zendesk's native test tools to check behavior and conversation flow, then review each answer against Guide articles, macros, ticket history, policy docs, escalation rules, and launch decisions by customer intent. - FAQ: Is the Zendesk Test AI agent button enough? It is useful, but not enough by itself. Support teams still need to validate source evidence, macro conflicts, high-risk edge cases, and whether each intent should be approved, restricted, blocked, or fixed before launch. - FAQ: What Zendesk sources should be audited for AI readiness? Audit Zendesk Guide articles, shared macros, recent tickets, ticket tags, internal policy docs, escalation notes, use cases, actions, and conversation logs from pilot testing. ### Customer Service QA for AI Support Agents [Customer Service QA for AI Support Agents](https://www.meihaku.com/articles/customer-service-qa-ai-support-agents): A practical guide for turning customer service QA into an AI support quality program that reviews source evidence, policy safety, escalation, and re-contact risk. - Audience intent: Customer service QA. - Published: 2026-05-09. - Updated: 2026-05-09. - Customer service QA used to mean sampling human-agent conversations, scoring tone and process, then coaching the team. AI support changes the job. The QA program now has to review answers that come from models, retrieval systems, workflow rules, and source documents. - The central question is not whether the AI sounded helpful. It is whether the AI was allowed to answer, used the right source, included the right conditions, escalated at the right time, and avoided creating a customer-facing policy mistake. - Use this guide to adapt customer service quality assurance for AI support agents, whether the runtime agent is Intercom Fin, Zendesk AI, Gorgias AI, Decagon, Sierra, or a custom support stack. - If your team calls the function support QA, CX QA, or call center quality assurance, the same operating shift applies: keep the human scorecard, then add source evidence and launch-scope decisions. - What changes when QA reviews an AI support agent: Human-agent QA usually starts with a transcript. AI support QA has to start one layer earlier: the source boundary. A human agent can remember a new policy, ask a teammate, or judge when a case is unusual. An AI support agent needs the right source material and an explicit rule for when to stop. That changes the scorecard. Tone still matters, but tone is not the launch blocker. The higher-risk checks are source grounding, policy fit, escalation, privacy, account-specific judgment, and whether the answer would survive review by a support lead. The unit of review should be the customer intent. A single answer can look good in isolation while the intent remains unsafe because the help article is stale, the macro says something different, or the escalation path is missing. - Build an AI support QA scorecard: A useful AI support QA scorecard should be short enough to use every week and strict enough to catch wrong-answer risk. Keep the scoring dimensions tied to what customers and support leaders actually experience. Start with eight checks: intent match, answer accuracy, source evidence, policy conditions, escalation, privacy, tone, and resolution. This should sit beside your existing customer service quality assurance scorecard rather than replace it. Then add a launch decision: approved, restricted, blocked, or source-fix needed. This extra decision matters. A response can score well on tone and accuracy but still be restricted because the answer depends on plan, region, customer tier, order status, identity verification, or a human approval step. - Sample AI support QA rubric: The QA rubric below works for both pre-launch testing and post-launch review. The exact labels can change, but the operating idea should not: every reviewed AI answer needs both a quality score and a scope decision. Approved means the intent is source-backed, low-risk, and safe for the AI to handle inside the current boundary. Restricted means the AI can answer only after a required check, such as plan, region, account state, order status, or customer tier. Blocked means the AI should not answer because the source is missing, stale, conflicting, private, or too risky. Source fix means the answer might become automatable after the knowledge owner updates the article, macro, SOP, or policy. - FAQ: How is AI support QA different from traditional customer service QA? Traditional QA reviews human-agent behavior. AI support QA also reviews source evidence, retrieval, policy boundaries, escalation rules, and whether the AI should have answered the intent at all. - FAQ: What should be on an AI support QA scorecard? Include intent match, accuracy, source evidence, policy conditions, privacy, escalation, tone, resolution, and a launch decision such as approved, restricted, blocked, or source-fix needed. - FAQ: Is deflection enough to measure AI support quality? No. Deflection should be paired with verified resolution, re-contact rate, wrong-answer review, escalation success, and human override review. ### AI Support Compliance Checklist Before Launch [AI Support Compliance Checklist Before Launch](https://www.meihaku.com/articles/ai-support-compliance-checklist): A practical compliance-readiness checklist for support, legal, security, and risk teams reviewing customer-facing AI support before launch. - Audience intent: AI support compliance. - Published: 2026-05-09. - Updated: 2026-05-09. - AI support compliance is not a checkbox at the end of vendor selection. It is the operating proof that the AI is only answering the customer intents your team has reviewed, sourced, and approved. - For support leaders, the compliance question is practical: can we show what the AI was allowed to answer, which source supported it, who approved the boundary, what stayed human-owned, and how failures are reviewed after launch? - Use this checklist before launching Intercom Fin, Zendesk AI, Gorgias AI, Decagon, Sierra, Maven, or a custom support agent. It is not legal advice; it is a readiness workflow for the evidence legal, security, compliance, and support teams usually ask for. - Start with the AI support launch boundary: A compliance review should not begin with a broad claim that the AI is safe. It should begin with the exact customer intents the AI is allowed to handle. Low-risk how-to questions, billing education, refund decisions, account access, regulated complaints, and legal threats do not carry the same exposure. For every intent, record one of five decisions: approved, restricted, blocked, source-fix needed, or human-only. This turns compliance from a vague policy conversation into an operating map the support team can follow. The launch boundary should be narrow enough to defend. If an answer needs account-specific judgment, identity verification, financial advice, legal interpretation, medical guidance, or a policy exception, the AI should not own the final answer without a human-approved workflow. - Require source evidence for every approved answer: The most useful compliance artifact is the source trail behind each approved answer. If the AI tells a customer they are eligible, ineligible, owed a refund, blocked from access, or required to complete a step, the team needs to know which source allowed that answer. Source evidence can come from help-center articles, macros, SOPs, policies, product docs, Google Docs, ticket patterns, or an approved answer set. The key is that the source must include the exact condition the AI repeats to the customer. When sources disagree, the intent should be blocked until the team chooses a canonical answer. A model should not be forced to infer compliance policy from contradictory support content. - Separate information from regulated judgment: Many support questions look simple until the customer asks for a decision. Explaining where to find a policy is different from applying that policy to an account. Explaining how a claim, refund, or dispute process works is different from deciding eligibility. The compliance checklist should mark the point where an answer moves from general information to regulated, account-specific, or exception-based judgment. That point is usually where the AI should clarify, escalate, or hand off. This distinction is especially important for fintech, insurtech, healthtech, education, telecom, and marketplaces where customer support can touch rights, payments, identity, safety, or regulated complaints. - FAQ: What is an AI support compliance checklist? It is a launch-readiness checklist that proves which customer intents an AI support agent may answer, which sources support those answers, what remains human-owned, and how failures are reviewed after launch. - FAQ: Does this replace legal or compliance review? No. This checklist organizes the evidence legal, compliance, security, and support teams need. Those teams still decide what is safe for their business and jurisdiction. - FAQ: What AI support topics should stay human-owned? Legal threats, regulated complaints, medical or financial advice, identity changes, payment disputes, security incidents, high-value exceptions, and account-specific judgment should usually stay human-owned unless a reviewed workflow exists. ### Helpdesk AI Vendor Comparison: What to Check Before You Choose [Helpdesk AI Vendor Comparison: What to Check Before You Choose](https://www.meihaku.com/articles/helpdesk-ai-vendor-comparison): A practical helpdesk AI vendor comparison checklist for support teams choosing between native helpdesk AI, AI-first support agents, and custom automation. - Audience intent: Helpdesk AI comparison. - Published: 2026-05-09. - Updated: 2026-05-09. - A helpdesk AI vendor comparison should not start with model demos. It should start with your support operation: sources, ticket history, workflows, channels, escalation rules, compliance constraints, and the customer intents you want AI to own. - Intercom Fin, Zendesk AI, Gorgias AI, Decagon, Sierra, and custom support agents solve different runtime problems. The shared buying risk is that teams compare vendor surfaces before proving their knowledge base and policies are ready for any AI agent. - Use this checklist to compare AI customer service software without overfitting to a demo. The output should be a launch map: which intents are approved, restricted, blocked, source-fix needed, or human-only before the vendor goes live. - Compare the operating model first: Most helpdesk AI vendors fall into three practical categories. Native helpdesk AI lives inside the system your agents already use, such as Intercom, Zendesk, or Gorgias. AI-first support platforms focus on customer-facing agents across workflows and channels. Custom agents give the team more control but require more engineering and governance. The category matters less than the launch boundary. A vendor can be strong and still fail if the source material is stale, macros disagree with policies, or high-risk intents are pushed into automation too early. Before scoring vendors, write down the operating job you need: deflect simple FAQs, automate ecommerce order actions, support complex enterprise workflows, replace a legacy chatbot, or govern AI answers in a regulated queue. - Check source readiness before vendor fit: Every AI helpdesk comparison should ask whether the vendor can see the right sources and whether those sources are good enough. The AI may read help-center articles, macros, snippets, SOPs, Google Docs, product pages, Shopify data, ticket history, or internal policy documents. The hard question is not whether a source can be connected. It is whether the source contains one current, complete, customer-safe answer for each important intent. If the same refund, billing, cancellation, access, warranty, security, or escalation question has conflicting answers across sources, the vendor comparison is premature. The first job is choosing the canonical answer and blocking the intent until it is fixed. - Test with real customer questions: Demo prompts make every AI support agent look better than the ticket queue will. A serious vendor comparison should use recent tickets, chats, failed searches, macro usage, and high-risk edge cases. Intercom documents Batch Test for running real questions and inspecting content, guidance, automations, language behavior, and answer ratings before deployment. Zendesk documents native AI-agent testing, test widgets, conversation logs, and AI-agent ticket review. Those surfaces are useful, but the support team still needs a consistent rubric across vendors. The first comparison set should include 50 to 150 questions across high-volume and high-risk intents. Preserve messy phrasing, missing context, angry customers, multi-intent requests, and compliance-sensitive edge cases. - FAQ: How should we compare helpdesk AI vendors? Compare vendors with the same historical support questions, source evidence, policy checks, workflow actions, handoff tests, and launch decisions. Do not rely only on demo prompts or generic accuracy claims. - FAQ: Should we choose native helpdesk AI or an AI-first support agent? Native helpdesk AI is often faster when your current helpdesk owns the sources and workflows. AI-first platforms may fit better when the agent spans channels, systems, and complex customer workflows. The right choice depends on launch scope and operational readiness. - FAQ: What should block an AI helpdesk vendor rollout? Missing source evidence, conflicting policies, unsafe workflow actions, weak handoffs, regulated judgment, account-specific decisions, and no post-launch QA loop should block broad automation. ### AI Agent Testing for Customer Support: Pre-Launch Readiness Checklist [AI Agent Testing for Customer Support: Pre-Launch Readiness Checklist](https://www.meihaku.com/articles/ai-agent-testing-customer-support): A support-specific AI agent testing checklist for policy coverage, source citations, stale answers, escalation rules, and launch go/no-go decisions. - Audience intent: AI agent testing. - Published: 2026-04-29. - Updated: 2026-05-09. - Most AI agent testing advice is written for engineers testing tool calls, latency, or model behavior. Support leaders need a different test: can this AI answer customers without creating policy, trust, or escalation risk? - A passing support AI test is a correct, sourced, policy-safe resolution that knows when to stop and hand off, not just a clever-sounding reply. The practical question is simple: what has to be true before this support AI can answer customers? - This checklist is written for support operations, CX leaders, and knowledge owners preparing to launch AI agents in Intercom Fin, Zendesk AI, Gorgias AI, Salesforce Agentforce, Decagon, Sierra, or a custom support stack. - What AI agent testing means in customer support: AI agent testing in customer support means validating whether an AI support agent can handle customer intents accurately, with source evidence, correct policy handling, and safe escalation before reaching customers. This is a different bar than a generic LLM evaluation. If the internal question is how to test AI support agent before launch, start here: run live support intents from your queue, grade the answers against approved sources, and decide which topics the AI can safely own. AI chatbot testing often focuses on conversation flow; AI agent testing must also prove policy safety, source grounding, and escalation behavior. A model benchmark might ask whether an answer is fluent, coherent, or close to a reference answer. A support readiness test asks whether the answer is allowed, current, cited, complete, and safe for the customer in front of you. A beautiful answer that invents a refund exception is a failed support answer. The unit of testing should be the customer intent, not a prompt. A prompt is one phrasing. An intent is the support job underneath many phrasings: cancel my subscription, explain this charge, reset my account, check a shipping delay, request a refund, recover access, delete my data, or ask for a human. - Build the test set from historical tickets: The strongest AI agent testing set starts with the questions customers already ask. Export recent tickets, chats, and macros from the last 60 to 90 days. Group them into the top support intents, then add the high-risk intents that may not be high-volume but can create financial, legal, or trust damage if answered incorrectly. Do not polish the language before testing. Customers do not write test prompts. They write incomplete, emotional, ambiguous messages. Keep the messy wording, missing context, typos, and multi-intent messages. If the AI only passes on clean internal examples, it will fail in the inbox. A practical first set is 100 to 250 questions across the top 25 to 50 intents. That is enough to reveal source gaps, stale policies, and weak handoff rules without turning the first launch review into a research project. - Map each test to source evidence: Every test question should map to a source of truth before the AI answer is graded. That source may be a help-center article, macro, SOP, policy document, knowledge base entry, pricing page, or approved answer. If the team cannot identify the source, the AI should not be cleared to answer the intent. This is where many teams discover the actual blocker. The AI agent may be capable, but the support operation is not. A refund rule may exist in three places. A macro may be newer than the public article. A senior agent may know the current exception process, but the knowledge base does not. Testing without source evidence turns reviewers into judges of plausibility. Testing with source evidence turns the review into an operational decision: ready, restricted, or blocked. - FAQ: What is AI agent testing for customer support? AI agent testing for customer support validates whether an AI support agent can answer customer intents accurately, with source evidence, correct policy handling, safe escalation, and measurable resolution before reaching live customers. - FAQ: What should an AI agent testing framework include? A useful framework includes customer-intent coverage, source grounding, policy checks, escalation behavior, tone, resolution quality, wrong-answer review, and launch decisions such as pass, restrict, or block. - FAQ: Do AI agent testing tools replace support QA? No. AI agent testing tools can run batches, preserve sources, and surface failures, but support leaders still need to judge policy safety, escalation, customer context, and launch scope. ### AI Chatbot Testing Checklist: What to Verify Before Launch [AI Chatbot Testing Checklist: What to Verify Before Launch](https://www.meihaku.com/articles/ai-chatbot-testing-checklist): A practical chatbot testing checklist for support teams checking accuracy, policy safety, escalation, tone, and re-contact risk before launch. - Audience intent: AI chatbot testing. - Published: 2026-05-06. - Updated: 2026-05-06. - An AI chatbot can pass a demo and still fail in production. Demos usually test clean questions. Customers bring incomplete context, urgency, policy exceptions, old links, anger, and requests that cross several support topics at once. - A useful AI chatbot testing checklist proves the bot can resolve safe issues, recognize unsafe ones, cite the right source, and move customers to a human before the experience becomes a loop. - This guide is for customer service chatbot testing, not generic conversational AI testing. The difference matters because customer support answers can create refunds, cancellations, compliance risk, and churn. - Start with the launch scope: Before running chatbot testing, define what the chatbot is supposed to handle. A bot expected to answer password-reset questions needs a different test plan from a bot allowed to discuss refunds, cancellations, warranties, or account recovery. Create a launch scope with three columns: allowed topics, restricted topics, and blocked topics. Allowed topics are low-risk and source-backed. Restricted topics can be answered only under clear conditions. Blocked topics require human review or escalation. This keeps the chatbot testing checklist honest. You are not asking whether the bot can answer everything. You are asking whether it can answer the topics you are about to expose to customers. - Test coverage by support topic: List the support topics the chatbot is expected to answer. Each topic should have a source of truth, a customer-facing answer, and a risk level. If a topic does not have those three things, it should not be in the initial automation scope. Good coverage includes routine questions and painful edge cases. A chatbot that handles basic setup but fails billing disputes may still be useful, but only if billing is explicitly blocked or escalated. - Test source grounding: Every important answer should be traceable to a support article, macro, policy document, SOP, or approved ticket pattern. If the chatbot cannot show where an answer came from, the answer is harder to trust and harder to debug. Source grounding tests should include stale articles, duplicate policy pages, old plan names, and internal-only notes. The bot should not blend contradictory sources or expose internal shorthand to customers. AI chatbot testing tools should make this visible. If a tool cannot show which source supported the answer, support teams cannot tell whether a failure came from retrieval, stale content, policy ambiguity, or model behavior. - FAQ: What should be included in an AI chatbot testing checklist? Include launch scope, topic coverage, source grounding, policy boundaries, escalation, tone, prompt injection, re-contact risk, and a post-launch review process for wrong or unresolved answers. - FAQ: How is customer service chatbot testing different from generic chatbot testing? Customer service chatbot testing focuses on policy accuracy, source evidence, escalation, resolution, and customer risk. Generic chatbot testing often focuses more on conversation flow, latency, or tone. - FAQ: Is chatbot deflection a good launch metric? Only if it is paired with resolution and re-contact data. A chatbot can deflect customers who gave up, which looks good on a dashboard but damages customer experience. ### How to Audit Your Knowledge Base for AI Readiness [How to Audit Your Knowledge Base for AI Readiness](https://www.meihaku.com/articles/knowledge-base-ai-readiness-audit): A step-by-step AI knowledge base audit for finding stale articles, policy conflicts, missing intents, weak citations, and unsafe automation scope. - Audience intent: Knowledge-base audit. - Published: 2026-04-22. - Updated: 2026-04-22. - A knowledge base that works for human agents may not be ready for AI. Humans use memory, Slack, judgment, and context. AI agents retrieve what is written and treat it as operational truth. - An AI knowledge base audit asks whether each customer intent has one current, complete, source-backed answer that an AI agent can safely use. If the answer is missing, stale, contradictory, or written only for internal agents, the AI should not use it with customers. - The output of a good audit is a launch boundary, not a prettier help center: what your AI agent can answer, what it should restrict, and what it must hand off. - Is my knowledge base ready for AI?: Your knowledge base is ready for AI when each important customer intent has one current, complete, customer-safe answer with clear conditions and source ownership. Most teams discover they are only partially ready. A human support agent can work around a stale article by remembering the latest Slack update. An AI support agent cannot reliably do that unless the source material has been updated or the agent has a separate approved rule. The audit makes those hidden dependencies visible. Treat readiness as a question of evidence. If the AI cannot retrieve the correct source, cite it, and apply the right conditions, the intent is not ready for customer-facing automation. - Audit by customer intent: Do not start with article count. A large help center can still miss the twenty questions that create the most support risk. Start with the intents customers actually ask about, then map each intent to source evidence. Use recent tickets, macros, searches, and failed conversations to build the intent list. Include both high-volume and high-risk topics. A rare security or refund edge case may deserve more attention than a common low-risk how-to question. - Find stale and duplicate answers: Stale articles are not only old pages. A newer macro can make an older article stale. A pricing update can make a help-center screenshot wrong. A support exception can become tribal knowledge but never reach the canonical docs. Duplicate answers create retrieval risk. If an old refund page and a new macro both answer the same question differently, the AI may retrieve both or choose the wrong one. The audit should consolidate duplicates into one current source. Look for old plan names, old prices, old product screenshots, outdated compliance language, old shipping cutoffs, and articles with no named owner. - FAQ: What is an AI knowledge base audit? An AI knowledge base audit reviews whether your support knowledge can safely ground AI answers. It checks freshness, coverage, contradictions, source ownership, citation quality, and whether each answer is written clearly enough for machine retrieval. - FAQ: How do I know if my knowledge base is ready for AI? It is ready when each important customer intent has one current, complete, approved answer with conditions and source evidence. Conflicting policies or missing source evidence mean the intent should stay blocked. - FAQ: What should a chatbot knowledge base audit include? It should include intent coverage, stale content, duplicate articles, policy conflicts, source ownership, citation coverage, internal-only notes, and escalation rules for topics the chatbot should not answer. ### How to Test Intercom Fin Before Launch [How to Test Intercom Fin Before Launch](https://www.meihaku.com/articles/how-to-test-intercom-fin-before-launch): A practical Intercom Fin pre-launch testing workflow for support teams that need to prove source coverage, procedures, and escalation before customers see answers. - Audience intent: Intercom Fin testing. - Published: 2026-05-09. - Updated: 2026-05-09. - Testing Intercom Fin before launch is not a prompt-writing exercise. It is a launch-boundary exercise: which customer intents can Fin answer with current source evidence, and which intents still need cleanup or human ownership? - Intercom Batch Test gives teams a way to run real customer questions before deployment. Meihaku adds the support-readiness layer around that run: source coverage, policy conflicts, procedure triggers, and pass, restrict, or block decisions by intent. - Start with the launch boundary: Before opening Batch Test, write down what Fin is allowed to answer on day one. Keep the first boundary narrow: common how-to questions, low-risk billing explanations, simple account navigation, and topics with one current source of truth. Then list what Fin should not answer yet. This usually includes refunds with exceptions, contract changes, access recovery, security requests, legal language, reseller billing, high-value accounts, and anything where agents still use private judgement. - Build the question set from real support data: Use recent conversations and topic groups first because they reflect the language customers actually use. Then add a CSV group for the edge cases that may not appear often but define launch risk: cancellation timing, billing exceptions, admin access, data deletion, and escalation requests. Intercom supports question generation from past conversations, topic-based question groups, manual questions, and CSV upload. A strong pre-launch run uses more than one method: real conversation phrasing for coverage, and curated CSV questions for risk. - Inspect the source and automation path: A Fin answer should not pass because it sounds reasonable. It passes when it used the right help article, snippet, guidance, procedure, data connector, custom answer, or automation path for the customer intent. Record source failures separately from writing failures. If Fin used the wrong article, the fix is source routing or content cleanup. If it used the right source but skipped a condition, the fix may be guidance, procedure logic, or a clearer article. - FAQ: What is the best way to test Intercom Fin before launch? Use Batch Test with real conversation questions, topic-based groups, and curated CSV edge cases. Grade each result against source evidence, policy fit, completeness, escalation, and launch decision. - FAQ: How many Intercom Fin questions should we test? Start with 30-50 questions per test group, then create separate groups for high-volume intents, high-risk intents, regions, plans, and brands when the answer changes by segment. - FAQ: Should Intercom Fin answer every topic it can answer? No. Some answers can be technically correct but still unsafe to automate because they depend on account context, legal exposure, security risk, or a judgement-heavy exception. ### Intercom Fin Testing Checklist for Support Teams [Intercom Fin Testing Checklist for Support Teams](https://www.meihaku.com/articles/intercom-fin-testing-checklist): A checklist for support and CX teams preparing Intercom Fin: what to test, what to inspect, and what should block customer-facing rollout. - Audience intent: Intercom Fin checklist. - Published: 2026-05-09. - Updated: 2026-05-09. - A useful Intercom Fin testing checklist does more than ask whether Fin answered correctly. It checks whether Fin used the right source, chose the right procedure, respected audience rules, handled ambiguity, and escalated when evidence ran out. - Use this checklist as a pre-launch QA pass and as a recurring regression test after content, guidance, procedure, or connector changes. - Content source checks: Every launch intent needs one defensible source. Before testing, identify the article, snippet, guidance, procedure, or internal note Fin should use. If reviewers cannot name the source, the intent is not ready for automation. During review, check whether the answer contains all material conditions from the source: eligibility, timing, plan, region, account status, proof requirement, and escalation trigger. - Audience, brand, and language checks: Fin can behave differently by audience, brand, language, and available content. Test the same intent across the segments where the answer changes. A refund, cancellation, or access answer that works for one brand may be wrong for another. Treat missing language settings as a launch blocker when customers will contact support in that language. A translated answer still needs the same source and policy standard as the default-language answer. - Procedure trigger checks: Procedures are powerful when the trigger is precise. Test both positive and negative examples: messages where the procedure should start, and similar messages where it should not. False triggers create confusing handoffs and unsafe actions. For procedures that call data connectors or perform actions, test missing data, connector failure, empty response, customer refusal, and handoff paths. The fallback matters as much as the happy path. - FAQ: What should be on an Intercom Fin testing checklist? Include source coverage, answer accuracy, policy fit, audience and brand targeting, language behavior, procedure triggers, connector fallback, escalation rules, and launch decision by intent. - FAQ: How often should we retest Intercom Fin? Retest after major help article, snippet, guidance, procedure, connector, policy, pricing, language, or brand changes. Keep recurring test groups for top and high-risk intents. - FAQ: What should block an Intercom Fin launch? Missing source evidence, conflicting policies, stale content, unsafe procedure triggers, poor handoff context, and high-risk account or security requests should block broad automation. ### Intercom Fin vs Zendesk AI for Support AI Rollout [Intercom Fin vs Zendesk AI for Support AI Rollout](https://www.meihaku.com/articles/intercom-fin-vs-zendesk-ai-support-rollout): A practical comparison for support teams deciding how to test and govern Intercom Fin or Zendesk AI before customer-facing rollout. - Audience intent: Vendor rollout comparison. - Published: 2026-05-09. - Updated: 2026-05-09. - Intercom Fin and Zendesk AI are not the same rollout problem. The vendor choice matters, but the launch risk is usually the same underneath: weak sources, conflicting policies, unclear escalation, and no approved boundary for what the AI can answer. - Use this comparison to decide what to test before rollout, not to pick a winner from a feature checklist. Meihaku sits around either vendor as the readiness layer that proves what is safe to automate. - The main difference is where operational truth lives: Intercom Fin teams often start by testing Fin-visible content, guidance, procedures, topics, and automations. Zendesk AI teams often need to reconcile Guide articles, shared macros, historical ticket patterns, AI agent tickets, and agent workflows. That means the readiness audit should follow the support operation. If agents trust macros more than articles, the Zendesk audit must check macro drift. If Fin Procedures control a complex flow, the Intercom audit must check trigger logic and fallback behavior. - How to test Intercom Fin rollout readiness: For Intercom Fin, start with Batch Test groups. Use recent conversations for real phrasing, topic groups for coverage, manual questions for edge cases, and CSV upload for repeatable launch-risk scenarios. Then inspect the answer evidence. Did Fin use the expected content source? Did a procedure trigger only when it should? Did a connector or automation fire safely? Did Fin ask for missing context before answering? - How to test Zendesk AI rollout readiness: For Zendesk AI, start with the support artifacts that already guide agents: Guide articles, shared macros, ticket tags, AI agent tickets, and internal policies. Suggested macros and AI agent reviews both depend on what happened in real tickets, so bad historical patterns can become scaled behavior. The key audit question is whether the source set agrees. If Guide says one refund rule, the macro says another, and recent tickets show a third exception pattern, AI should not be allowed to answer that intent until the canonical source is fixed. - FAQ: Is Intercom Fin or Zendesk AI easier to test before launch? It depends on where your support truth lives. Intercom Fin testing often centers on Batch Test, content, guidance, procedures, and automations. Zendesk AI testing often requires Guide, macro, ticket, and bot-ticket review. - FAQ: Can Meihaku be used with both Intercom Fin and Zendesk AI? Yes. Meihaku is the readiness layer around the support stack. It does not replace the AI agent; it audits whether each customer intent has source evidence and a safe launch decision. - FAQ: What should decide rollout scope? Rollout scope should be decided by approved intents, not vendor confidence. Each intent needs source evidence, policy fit, escalation behavior, and a clear approve, restrict, block, or human-only decision. ### AI Support Readiness: The Operational Framework for Support Teams [AI Support Readiness: The Operational Framework for Support Teams](https://www.meihaku.com/articles/ai-support-readiness): A practical six-dimension framework for auditing knowledge, policies, testing, handoffs, owners, and metrics before an AI support agent answers customers. - Audience intent: AI support readiness. - Published: 2026-04-15. - Updated: 2026-05-09. - Most support teams are not blocked by the model. They are blocked by the operating system underneath the model: stale knowledge, conflicting policies, weak testing, unclear escalation, and no owner for AI answer quality. ## Integrations ### Intercom Fin [Intercom Fin AI Support Readiness Audit](https://www.meihaku.com/integrations/intercom): Use Meihaku before and alongside Intercom Fin to decide which customer intents are safe to automate, which need source cleanup, and which should stay human-only. - Primary intent: Intercom Fin AI support readiness. - Sources audited: Fin content sources and public help articles; Resolution paths, snippets, and internal support notes; Recent conversations grouped by customer intent; Policies for billing, refunds, access, security, and escalation. - Key risks: Fin answers from a public article while agents still follow a newer internal policy.; A billing or cancellation question looks covered, but the exception conditions are missing.; Escalation rules are unclear, so customers repeat the same problem to the bot and then a human.; The team only tests polished prompts instead of messy customer language from real conversations.. - Map Fin content to live customer intents: Meihaku groups real support questions, then checks whether Intercom-visible content has a current, complete answer for each intent. - Find source conflicts before Fin blends them: When a help article, snippet, and internal note disagree, reviewers see the conflict before the AI agent has to choose. - Approve the launch boundary: Only approved, cited intents become launch-ready. Missing, stale, or high-risk intents stay restricted or blocked. ### Zendesk AI [Zendesk AI Support Readiness Audit](https://www.meihaku.com/integrations/zendesk): Use Meihaku to audit whether Zendesk Guide, macros, ticket history, and policy documents are ready for Zendesk AI to answer customers. - Primary intent: Zendesk AI audit. - Sources audited: Zendesk Guide articles and help center content; Macros, triggers, and escalation notes; Recent tickets and tags by contact reason; Google Drive or internal policy documents used by agents. - Key risks: Guide says one refund window while macros say another.; Zendesk AI sees a short article but misses the internal exception policy.; Ticket tags are too broad to prove which intents are safe for automation.; The AI passes easy help-center questions but fails billing, account access, or security edge cases.. - Audit Guide and macros together: Meihaku reads the Zendesk sources agents actually use, then shows whether each customer intent has one defensible answer. - Use tickets as the test set: Recent tickets provide messy wording, missing context, multi-intent questions, and high-risk edge cases a polished test set misses. - Gate Zendesk AI by approved intent: Ready intents can move toward automation. Conflicted, stale, or missing-source intents stay blocked until the source evidence is fixed. ### Gorgias AI [Gorgias AI Accuracy and Readiness Audit](https://www.meihaku.com/integrations/gorgias): Use Meihaku to check whether ecommerce support knowledge is ready for Gorgias AI before it handles refund, order, shipping, and product questions. - Primary intent: Gorgias AI accuracy. - Sources audited: Gorgias macros, guidance, and support conversations; Product, shipping, refund, warranty, and return policies; Shopify or ecommerce operational notes used by agents; High-risk intents from VIP, fraud, damaged-order, and exception cases. - Key risks: The AI gives a generic refund answer that misses product-specific exclusions.; Shipping status and delivery-delay guidance differ across policy pages and macros.; VIP or fraud-sensitive tickets need a human, but the handoff trigger is not explicit.; Product-availability answers are stale because the help content does not match operations.. - Turn ecommerce tickets into launch tests: Meihaku groups order, refund, shipping, product, and warranty questions so Gorgias AI is tested against the support queue customers actually create. - Check policy and product specificity: The audit looks for the conditions and exclusions that make ecommerce answers risky: final sale, regional shipping, bundle rules, warranty terms, and account-specific judgement. - Separate safe automation from human-only work: Approved intents can move toward Gorgias AI. Exceptions, policy conflicts, and judgement-heavy cases stay human-owned. ### Google Docs [Google Docs AI Support Readiness Audit](https://www.meihaku.com/integrations/google-docs): Use Meihaku to audit support policies, SOPs, macros, and FAQ documents stored in Google Drive before an AI support agent relies on them. - Primary intent: Google Docs AI support readiness. - Sources audited: Google Drive folders selected by the support or knowledge team; Google Docs exported into the sandbox source bundle for review; SOPs, policy docs, FAQ drafts, and internal support notes; Source citations tied back to the document and customer intent. - Key risks: A policy doc in Drive is newer than the help center, but no one has made it canonical.; A Google Doc contains internal-only exceptions that should not be exposed to customers.; Several SOP drafts answer the same customer question with different conditions.; The AI agent sees a cleaned help article but misses the operational details agents use from Drive.. - Select the support knowledge folder: Meihaku reads the Google Drive folder your team chooses, then turns supported documents into a sandbox source bundle for the audit. - Map Docs content to customer intents: The readiness map shows which customer questions have source evidence in Google Docs and which rely on stale, conflicted, or missing material. - Approve what AI can reuse: Reviewers approve only the answers supported by current, customer-safe source evidence. Draft-only or internal-only content stays out of automation. ### Help Scout [Help Scout AI Answers Readiness Audit](https://www.meihaku.com/integrations/help-scout): Use this readiness workflow to check whether Help Scout Docs, AI Answers knowledge sources, Beacon flows, and support conversations are safe for customer-facing AI. - Primary intent: Help Scout AI Answers readiness. - Sources audited: Help Scout Docs articles, collections, and categories; AI Answers knowledge sources and AI Agent settings; Beacon paths, suggested questions, and customer sessions; Private collections, improvements, and human handoff rules. - Key risks: AI Answers pulls from Docs, but the article omits the exception agents use in conversations.; Private collections contain internal-only notes that should not appear in a customer-facing answer.; Beacon mode pushes self-service before the team has approved which intents are safe.; Improvements fix one conversation but do not create a governed source owner or retest path.. - Map Docs to real Help Scout questions: Group recent Help Scout conversations and Beacon questions into customer intents, then check whether Docs has current source evidence for each one. - Review AI Answers sources and improvements: Treat additional sources and improvements as support knowledge that needs owner, scope, and customer-safe language before broad exposure. - Approve Beacon launch scope: Move only source-backed intents into AI Answers. Missing, private, conflicted, or high-risk topics stay human-owned. ### Front [Front AI Knowledge Base Readiness Audit](https://www.meihaku.com/integrations/front): Use this readiness workflow to review whether Front knowledge base content and customer conversation history can safely ground AI support answers. - Primary intent: Front AI knowledge base readiness. - Sources audited: Front knowledge base articles and help center content; Internal and external knowledge base boundaries; Customer conversation history and applied policy patterns; Copilot, Autopilot, handoff, and escalation context. - Key risks: AI uses conversation history that reflects an old workaround rather than current policy.; Internal and external knowledge are both relevant, but the customer-facing boundary is unclear.; Front AI can draft or update content, but the team has not approved the source behind risky answers.; Complex policies are documented, but the AI lacks a clear handoff rule for exceptions.. - Separate internal and customer-facing knowledge: Review which Front knowledge base articles can ground customer answers and which internal procedures should remain reviewer-only. - Use conversations as evidence, not policy: Conversation history can reveal how agents solve issues, but support leaders still need one canonical source for the AI to reuse. - Build the approved answer boundary: Approve low-risk intents, restrict context-heavy answers, and keep disputed or judgment-heavy work in a human queue. ### Notion [Notion AI Support Readiness Audit](https://www.meihaku.com/integrations/notion): Use this readiness workflow when support policies, SOPs, FAQs, release notes, and escalation guidance live in Notion before AI support launch. - Primary intent: Notion AI support readiness. - Sources audited: Notion pages, wikis, databases, and linked support docs; SOPs, policy drafts, launch notes, and internal FAQs; Owner, status, review date, and version metadata; Exported or approved Notion content used as source evidence. - Key risks: A Notion page is the current policy, but the help center still says something older.; Draft and approved guidance live in the same workspace with no clear customer-facing boundary.; AI retrieves an internal note that includes shorthand or sensitive exception language.; Support relies on a Notion database, but stale rows have no owner or review date.. - Separate drafts from approved support policy: Mark which Notion pages are canonical, draft, archived, internal-only, or unsafe for customer-facing AI answers. - Map pages to customer intents: Group recent support questions and attach the Notion page or database entry that should support each answer. - Turn Notion cleanup into launch scope: Approve only current, owner-reviewed answers. Missing, stale, or conflicting Notion content becomes source-fix work. ### Confluence [Confluence AI Support Readiness Audit](https://www.meihaku.com/integrations/confluence): Use this readiness workflow when support policies, troubleshooting articles, SOPs, and internal knowledge base spaces live in Confluence. - Primary intent: Confluence AI support readiness. - Sources audited: Confluence spaces, pages, live docs, blogs, and files; Knowledge base spaces, how-to pages, and troubleshooting articles; Page labels, owners, permissions, review dates, and archived content; Jira Service Management-linked support knowledge where applicable. - Key risks: The AI sees a Confluence page that is technically searchable but no longer canonical.; Space permissions mix internal-only policy with content that could be customer-facing.; Troubleshooting pages omit escalation rules for regulated, security, or account-specific issues.; Several child pages answer the same support intent with different conditions.. - Choose the support knowledge spaces: Start with the Confluence spaces and pages support agents already trust for policy, troubleshooting, and escalation. - Audit permissions and ownership: Review which pages are customer-safe, internal-only, stale, archived, or ownerless before they become AI source evidence. - Approve one answer per support intent: Resolve duplicate or conflicting Confluence pages into a launch boundary the AI can defend with citations. ### Service Cloud / Agentforce [Salesforce Service Cloud AI Readiness Audit](https://www.meihaku.com/integrations/salesforce-service-cloud): Use this readiness workflow to check whether Salesforce Knowledge, Service Cloud cases, Agentforce actions, and support policies are safe for customer-facing AI. - Primary intent: Salesforce Service Cloud AI readiness. - Sources audited: Salesforce Knowledge articles and service documentation; Service Cloud cases, case reasons, macros, and quick text; Agentforce Service Agent topics, actions, prompts, and handoff rules; Policies for billing, access, security, compliance, and regulated escalation. - Key risks: Agentforce can answer with Salesforce Knowledge, but the article does not include the exception agents apply in cases.; A standard action or workflow is available, but the source policy does not define when AI should invoke it.; Case history reveals common contact reasons, while the canonical article is stale or incomplete.; The Service Cloud rollout approves automation before sensitive, regulated, or account-specific intents have a human handoff path.. - Map cases to Salesforce Knowledge: Group recent Service Cloud cases by customer intent, then attach the Knowledge article, quick text, or policy source that should ground each answer. - Review Agentforce action boundaries: Check which topics and actions are safe for self-service, which need reviewer control, and which should stay human-owned. - Approve one source-backed answer per intent: Launch-ready intents need a current source, clear conditions, and a defensible escalation rule before AI handles them. ### Freshdesk / Freddy AI [Freshdesk Freddy AI Readiness Audit](https://www.meihaku.com/integrations/freshdesk): Use this readiness workflow to check whether Freshdesk solution articles, ticket patterns, Freddy AI Agent knowledge sources, and workflows can safely support AI answers. - Primary intent: Freshdesk Freddy AI readiness. - Sources audited: Freshdesk solution articles and knowledge base categories; Freddy AI Agent knowledge sources, workflows, skills, and test scenarios; Tickets, canned responses, tags, and repeated contact reasons; Policies for refunds, access, service levels, handoffs, and unsupported actions. - Key risks: Freddy AI retrieves a broad solution article that covers several topics and gives a partial answer.; A workflow can resolve a simple case, but the source policy does not define exception handling.; Canned responses and solution articles answer the same customer intent with different conditions.; The AI Agent test set uses clean examples instead of the messy wording in real Freshdesk tickets.. - Turn Freshdesk tickets into AI test intents: Group recent tickets, tags, and canned response usage into customer intents, then check whether each one has an approved source. - Audit solution articles for AI retrieval: Review whether articles are focused, complete, current, and explicit about requirements, limits, unsupported paths, and escalation. - Gate Freddy AI by approved workflow scope: Approve low-risk intents, restrict workflows with missing conditions, and keep complex or judgment-heavy questions in the human queue. ### HubSpot Customer Agent [HubSpot Customer Agent Readiness Audit](https://www.meihaku.com/integrations/hubspot): Use this readiness workflow to check whether HubSpot content, public URLs, tickets, and Service Hub knowledge are ready to ground Breeze-powered customer agent answers. - Primary intent: HubSpot Customer Agent readiness. - Sources audited: HubSpot knowledge base articles, website content, and public URLs; Customer Agent setup, channels, chatflows, and conversational context; Service Hub tickets, inbox conversations, and repeated customer questions; Policies for support scope, eligibility, escalation, and follow-up questions. - Key risks: The customer agent has existing content, but the content does not cover the conditions customers ask about.; Public URL content is available to AI, while internal policy has a newer or more restrictive answer.; The agent can answer from a verifiable source, but the source omits when to ask a follow-up question.; Support channels are assigned before the team has separated safe self-service from human-only intents.. - Map HubSpot content to support intents: Group tickets, inbox conversations, and chat questions into customer intents, then attach the HubSpot content or URL that should answer each one. - Review source eligibility and gaps: Check whether existing content is current, customer-safe, complete, and explicit about unsupported cases, eligibility, and handoff. - Approve the channel launch boundary: Decide which intents can be handled by the customer agent on each channel and which require clarification, restriction, or human escalation. ### Kustomer AI [Kustomer AI Readiness Audit](https://www.meihaku.com/integrations/kustomer): Use this readiness workflow to check whether Kustomer knowledge, CRM context, customer history, and AI Agent workflows can safely support autonomous CX answers. - Primary intent: Kustomer AI readiness. - Sources audited: Kustomer knowledge base content and internal support policies; Customer timeline, conversation history, CRM attributes, and intent data; AI Agent roles, tools, workflow logic, and external data source boundaries; Escalation rules, observability signals, and human-in-the-loop decisions. - Key risks: The AI has rich customer context, but the policy source behind the action is stale or ambiguous.; A workflow can act across systems, but the team has not approved when automation is safe.; Knowledge base, CRM data, and agent behavior imply different answers for the same support intent.; A handoff exists, but the conditions for sensitive, high-value, or exception-heavy requests are unclear.. - Separate context from policy: Use customer history and CRM data to understand the situation, then require an approved policy source for the customer-facing answer or action. - Audit AI Agent roles and tools: Review which roles, workflows, data sources, and actions each Kustomer AI Agent can use, then match them to approved customer intents. - Define the human-in-the-loop boundary: Approve routine intents, restrict high-context actions, and route conflicted or sensitive questions to a human before automation expands. ## Templates ### AI Support Launch Readiness Checklist [AI Support Launch Readiness Checklist](https://www.meihaku.com/templates/ai-support-launch-readiness-checklist): A vendor-neutral CSV checklist for deciding which customer intents are approved, restricted, blocked, or human-only before an AI support agent goes live. - Search intent: AI support launch readiness checklist. - Download: https://www.meihaku.com/templates/ai-support-launch-readiness-checklist.csv. - Use cases: Vendor-neutral AI support launch review; Knowledge-base cleanup backlog before automation; Support, legal, and operations go/no-go meeting. - List launch intents: Start with recent tickets, top contact reasons, and sensitive support topics instead of generic demo questions. - Attach proof: Record the source owner, evidence link, conflict check, test coverage, and required handoff rule for each intent. - Set the launch boundary: Mark every intent approved, restricted, blocked, source-fix-needed, or human-only before it reaches customer-facing AI. ### AI Agent Testing Framework Template [AI Agent Testing Framework Template](https://www.meihaku.com/templates/ai-agent-testing-framework-template): A vendor-neutral CSV template for testing customer-facing AI agents by intent, source evidence, policy fit, escalation behavior, reviewer workflow, and launch state. - Search intent: AI agent testing framework template. - Download: https://www.meihaku.com/templates/ai-agent-testing-framework-template.csv. - Use cases: AI agent testing framework for support teams; Pre-launch review of customer-facing AI answers; Reusable test set for source, policy, and escalation checks. - List intents: Group recent tickets and high-risk edge cases into customer intents instead of testing only polished prompts. - Grade the answer: Review each response against source evidence, policy conditions, completeness, tone, and escalation behavior. - Set launch state: Mark each intent approved, restricted, source-fix-needed, blocked, or human-only before customer exposure. ### Intercom Fin Batch Test CSV Template [Intercom Fin Batch Test CSV Template](https://www.meihaku.com/templates/intercom-fin-batch-test-csv): A launch-ready question set for Intercom Fin Batch Test. Upload the question column, then grade each response against source fit, missing policy detail, and safe escalation. - Search intent: Intercom Fin batch test CSV. - Download: https://www.meihaku.com/templates/intercom-fin-batch-test-questions.csv. - Use cases: Pre-launch Fin QA for a new workspace; Regression testing after policy or guidance changes; Executive review of high-risk customer intents. - Upload the question CSV: Use the single question column as the Batch Test input, then run the group with the audience, brand, and language settings that match launch. - Inspect the source behind each answer: Record whether Fin used the right article, snippet, guidance, procedure, or connector. Wrong source selection is a launch blocker. - Convert ratings into launch scope: Good answers can move toward approved scope. Poor or ambiguous answers become source fixes, procedure updates, or human-only intents. ### Zendesk AI Macro Audit Checklist [Zendesk AI Macro Audit Checklist](https://www.meihaku.com/templates/zendesk-ai-macro-audit): A checklist for auditing Zendesk Guide, shared macros, ticket patterns, and internal policies before using AI suggestions or customer-facing automation. - Search intent: Zendesk AI macro audit checklist. - Download: https://www.meihaku.com/templates/zendesk-ai-macro-audit.csv. - Use cases: Macro cleanup before enabling AI suggestions; Guide and macro conflict review before Zendesk AI rollout; Support QA review of high-volume contact reasons. - Export or list high-use shared macros: Prioritize macros applied to recent tickets, especially billing, access, returns, security, cancellation, and escalation topics. - Pair each macro with a Guide or policy source: A macro should not be the only source for a customer-facing AI answer unless the team intentionally treats it as canonical. - Assign an AI launch decision: Mark each topic approved, restricted, blocked, or source-fix-needed before exposing it to AI suggestions or automation. ### Gorgias AI Ecommerce Readiness Checklist [Gorgias AI Ecommerce Readiness Checklist](https://www.meihaku.com/templates/gorgias-ai-ecommerce-readiness): A practical ecommerce test matrix for deciding which Gorgias AI intents are safe to automate and which need better guidance, source evidence, or human handoff. - Search intent: Gorgias AI ecommerce readiness checklist. - Download: https://www.meihaku.com/templates/gorgias-ai-ecommerce-readiness.csv. - Use cases: Gorgias AI pre-launch review for Shopify stores; Returns, refunds, and damaged-order policy QA; Ecommerce support automation expansion planning. - List the ecommerce intents that create cost: Start with refunds, returns, shipping delays, damaged orders, warranties, subscriptions, cancellations, VIP exceptions, and fraud-sensitive account changes. - Attach the source and exception rule: For each intent, record the help center article, guidance, product catalog source, uploaded document, or action that makes the answer safe. - Decide coverage before expanding automation: Approve simple, source-backed topics. Restrict topics that need customer context. Hand off anything with fraud, legal, payment, or VIP judgement. ## Use Cases ### AI Support Pre-Launch Audit [AI Support Pre-Launch Audit](https://www.meihaku.com/use-cases/ai-support-pre-launch): A focused pre-launch audit for support leaders who need to decide which AI support intents can safely reach customers. - Audience: Support and CX leaders preparing a customer-facing AI rollout. - Problem: The AI agent is ready to demo, but the support operation has not proved which customer intents are safe enough for launch.. - Outcomes: Approved launch scope; Blocked intents with source-fix owners; Restricted intents with clear conditions; A retest list for the next rollout phase. - Map launch intents: Group recent tickets into customer intents and separate low-risk, restricted, blocked, and human-only work. - Attach source evidence: Connect each launch intent to the help article, macro, SOP, policy, or ticket pattern that proves the answer. - Approve the rollout boundary: Turn the audit into an approved-intent map so the AI launches on defensible scope instead of broad hope. ### AI Support Readiness for Regulated CX Teams [AI Support Readiness for Regulated CX Teams](https://www.meihaku.com/use-cases/regulated-industries-cx): A readiness workflow for fintech, insurtech, healthtech, and other regulated support teams evaluating customer-facing AI. - Audience: CX, compliance, risk, and operations teams in regulated markets. - Problem: Regulated teams cannot launch an AI support agent on unclear sources, undocumented approval, or weak escalation evidence.. - Outcomes: Audit-ready approved answer set; Human-only list for controlled or regulated topics; Source-fix backlog with owners; Post-launch review cadence. - Separate answerable from controlled topics: Identify which intents are informational, which need customer-specific checks, and which require human review. - Preserve source and approval evidence: Keep citations, reviewer decisions, scope notes, and blocked-intent reasons tied to each customer intent. - Define the governance loop: Assign owners for policy changes, source freshness, retesting, and post-launch wrong-answer review. ### AI Support Readiness for Ecommerce CX Operations [AI Support Readiness for Ecommerce CX Operations](https://www.meihaku.com/use-cases/ecommerce-cx-ops): A readiness workflow for ecommerce CX teams launching Gorgias AI, Zendesk AI, or other AI support tools across order and product questions. - Audience: Ecommerce CX operations teams expanding AI support coverage. - Problem: Ecommerce AI looks useful on FAQs, but launch risk lives in refunds, shipping exceptions, order changes, subscriptions, and product-specific answers.. - Outcomes: Safe ecommerce automation scope; Exception matrix for human review; Source-fix list for product and policy gaps; Regression tests for promotions and shipping changes. - Prioritize cost and risk intents: Start with refunds, returns, damaged orders, shipping delays, warranty, subscription, VIP, fraud-sensitive, and product compatibility questions. - Check source and action readiness: Pair each answer with the policy, product source, order context, or connected action needed before the AI responds. - Gate automation by exception risk: Automate low-risk answers, ask for context when needed, and hand off high-cost or fraud-sensitive cases. ### Policy Conflict Detection for AI Support [Policy Conflict Detection for AI Support](https://www.meihaku.com/use-cases/policy-conflict-detection): A workflow for finding policy contradictions that make AI support agents answer confidently from the wrong source. - Audience: Support operations and knowledge owners cleaning AI source risk. - Problem: AI agents do not know which policy is canonical when help articles, macros, SOPs, tickets, and internal notes disagree.. - Outcomes: Canonical answer by customer intent; Conflict list with source owners; Blocked intents until policy cleanup; Reduced wrong-source AI answers. - Group sources by customer intent: Compare every source that could answer the same customer question instead of reviewing documents one by one. - Flag contradictory conditions: Look for conflicting timing, eligibility, plan, region, proof, approval, or escalation language. - Approve one canonical answer: Choose the source-backed answer the AI is allowed to use and block the intent until the source set agrees. ### AI Agent Governance for Customer Support [AI Agent Governance for Customer Support](https://www.meihaku.com/use-cases/ai-agent-governance): A support-specific AI governance workflow for teams that need accountable source ownership and launch decisions by customer intent. - Audience: Support, operations, compliance, and AI program owners. - Problem: Without governance, AI support quality becomes nobody's job after launch, and source drift turns into customer-facing wrong answers.. - Outcomes: Clear ownership for AI answer quality; Governed rollout expansion; Evidence trail for support and compliance; Repeatable review loop after launch. - Assign intent owners: Every approved, restricted, blocked, and human-only intent needs an owner responsible for source freshness and retesting. - Track changes that affect answers: Product, pricing, policy, macro, article, and procedure changes should trigger review before AI scope expands. - Review failures weekly: Measure wrong answers, re-contact, human overrides, escalation misses, and newly discovered source conflicts. ### Support Knowledge Audit for AI Agents [Support Knowledge Audit for AI Agents](https://www.meihaku.com/use-cases/support-knowledge-audit): A practical support knowledge audit for teams preparing help centers, macros, SOPs, and ticket evidence for customer-facing AI. - Audience: Knowledge managers and support ops teams preparing AI support. - Problem: Most support knowledge was written for humans who can infer context. AI agents need current, explicit, source-backed answers by customer intent.. - Outcomes: AI-ready knowledge map; Source cleanup backlog; Approved answer set for downstream agents; Blocked intents until evidence improves. - Audit by intent, not by document: Start with the customer questions that drive support load, then check whether each has a complete answer. - Find stale and hidden knowledge: Identify outdated articles, private notes, duplicate macros, and ticket-only practices that never reached the help center. - Turn cleanup into launch scope: Approve source-backed intents and keep missing, stale, or contradictory intents out of automation. ## Comparisons ### Meihaku vs Intercom Fin [Meihaku vs Intercom Fin](https://www.meihaku.com/vs/intercom-fin): Intercom Fin is a runtime AI support agent. Meihaku is the readiness layer that checks whether the knowledge behind Fin is safe enough for customers before launch. - Primary intent: Meihaku vs Intercom Fin. - Best for Intercom Fin: Answering customer conversations inside Intercom; Using Fin content sources and Intercom workflows at runtime; Deflecting supported questions once the launch boundary is approved. - Best for Meihaku: Finding gaps, stale policies, and source conflicts before Fin answers; Turning recent conversations into approved, restricted, and blocked intents; Giving support leaders a cited launch-readiness map before rollout. - Primary job: Intercom Fin Answer customer questions in Intercom. Meihaku Audit whether those questions are ready for AI automation. - Timing: Intercom Fin Runs during customer conversations. Meihaku Runs before launch and during governance reviews. - Output: Intercom Fin Customer replies, resolutions, and handoffs. Meihaku Approved intent map, source gaps, conflicts, and citations. - Best buyer: Intercom Fin Teams ready to automate support inside Intercom. Meihaku Teams that need to prove what Fin is safe to answer first. ### Meihaku vs Decagon [Meihaku vs Decagon](https://www.meihaku.com/vs/decagon): Decagon is built to automate support interactions. Meihaku focuses on the source-readiness work that determines which support intents should be automated in the first place. - Primary intent: Meihaku vs Decagon. - Best for Decagon: Deploying a customer-facing AI support agent; Automating support workflows and resolutions at runtime; Running an AI agent across complex customer-service operations. - Best for Meihaku: Auditing whether support knowledge can safely ground AI answers; Detecting conflicting policies across help centers, macros, SOPs, and notes; Creating a defensible approved-answer boundary before vendor rollout. - Primary job: Decagon Automate customer support conversations. Meihaku Prove which support intents are safe enough to automate. - Failure mode addressed: Decagon Runtime resolution, escalation, and workflow handling. Meihaku Missing sources, source conflicts, stale content, and unsupported answers. - Output: Decagon Customer-facing agent behavior. Meihaku Readiness map, cited answers, and blocked-intent backlog. - Buying trigger: Decagon The team wants an AI agent to handle support. Meihaku The team needs confidence that its knowledge is ready for an AI agent. ### Meihaku vs Sierra [Meihaku vs Sierra](https://www.meihaku.com/vs/sierra): Sierra focuses on customer-facing AI agents. Meihaku helps support and CX teams prepare the knowledge, citations, and approval boundary those agents depend on. - Primary intent: Meihaku vs Sierra. - Best for Sierra: Launching branded customer-facing AI experiences; Automating customer interactions across service workflows; Operating AI agents in production channels. - Best for Meihaku: Checking whether support knowledge has one defensible answer per intent; Preserving citation and approval evidence for support and compliance teams; Separating approved, restricted, blocked, and human-only support scope. - Primary job: Sierra Create and operate customer-facing AI agents. Meihaku Audit support knowledge before and alongside those agents. - Risk focus: Sierra Runtime service experience and automation. Meihaku Source evidence, policy agreement, handoff rules, and approval scope. - Output: Sierra Customer interactions and automation workflows. Meihaku Cited readiness findings and approved answer boundaries. - Best buyer: Sierra Teams ready to build a branded AI customer agent. Meihaku Teams that need to de-risk what the agent will be allowed to say.