AI Chatbot Launch Checklist

Chatbots fail in public: a screenshot of a wrong answer, an invented policy, a promise nobody authorized, or a user stranded with no way to reach a person. Almost every such failure traces to a launch that skipped scope, grounding, guardrails, escalation, or evaluation. This checklist covers them. It complements how to build an ai chatbot, how to build a knowledge base chatbot, and why ai chatbots fail, and it reflects how FISTA Solutions launches conversational systems through its AI agents and AI enablement practices.

Who should use this checklist?

Product and engineering owners launching customer-facing or employee-facing chatbots, support and knowledge leaders whose content the bot uses, and reviewers from security, privacy, and compliance.

Is scope defined?

1. Audience and channel defined.
2. In-scope intents and topics listed from real question data.
3. Prohibited topics listed: legal, medical, financial advice, complaints handling, account changes without verification, and anything policy excludes.
4. Tone and language coverage defined.
5. Success measures agreed with a baseline where one exists.

Reference: how to write an ai spec.

Is knowledge curated and grounded?

Check	Evidence
Sources inventoried, curated, and owned; stale content retired	Curation record
Metadata for audience, product, region, effective date	Index schema
Permission filtering where audiences differ	Compliance tests
Hybrid retrieval with reranking	Architecture
Citations required and validated	Tests
Refusal on insufficient evidence with a defined message	Tests

Reference: the rag system launch checklist.

Are guardrails enforced outside the model?

1. Output validation blocks commitments, pricing not in approved content, prohibited advice, and sensitive disclosures.
2. Injection defenses for user messages and retrieved content.
3. Rate and length limits per conversation.
4. Identity verification before any account-specific information, where applicable.
5. Prompt instructions treated as a weak layer, not a control.

Reference: ai agent guardrails and the prompt injection defense checklist.

Is escalation designed?

1. Triggers: low confidence, prohibited topics, frustration, repeated failure, user request.
2. Handoff carries transcript and context to the human channel.
3. Human channel availability and hours communicated; out-of-hours behavior defined.
4. Ticket creation path where live handoff is unavailable.

Reference: ai agent human oversight.

Does evaluation pass?

1. Golden set of real questions by intent with approved answers, including edge cases and out-of-scope questions.
2. Metrics: answer accuracy, groundedness, citation validity, refusal correctness, escalation correctness.
3. Adversarial cases: injection, commitment elicitation, sensitive disclosure attempts, abuse.
4. Thresholds met, with the highest bar on prohibited topics.
5. Suite runs in CI for content, prompt, and model changes.

Reference: the ai evaluation checklist.

Are privacy and transparency handled?

1. Notice that users are talking to an AI, with a route to a human.
2. Data handling: what is stored, retention, redaction, provider terms.
3. Consent where required for personal data or recording.
4. Regulatory requirements for the audience and jurisdiction reviewed.

Reference: ai transparency notices and ai data privacy compliance.

Are analytics and quality sampling live?

1. Conversation analytics: volume by intent, resolution, escalation, satisfaction, abandonment.
2. Quality sampling with the golden-set graders and scheduled human review.
3. Unanswered and low-rated questions logged and routed to content owners.
4. Cost and latency per conversation tracked.
5. Alerts on quality drops, escalation spikes, and cost anomalies.

Reference: the ai observability checklist.

Is rollout staged?

1. Internal pilot with employees acting as users.
2. Limited external slice by segment, channel, or traffic percentage.
3. Expansion criteria on quality, escalation, and satisfaction.
4. Kill switch and rollback to the previous configuration.
5. Support team briefed on the bot's scope and failure modes.

Reference: ai pilot to production.

Is the operating model in place?

1. Named owners: engineering and business.
2. Runbooks for quality regression, provider outage, escalation overflow, and incidents.
3. Content update process with owners and cadence.
4. Change control through the evaluation gate.
5. Review cadence for metrics and the unanswered-question log.

Are agent capabilities, if any, additionally covered?

1. Tool permissions, approval gates, and identity verification for any actions.
2. Action evaluation in the harness.

Reference: the ai agent production readiness checklist.

How should gaps be handled?

Scope, grounding, guardrail, and evaluation gaps block any external exposure. Escalation and privacy gaps block launch beyond an internal pilot. Analytics and operations gaps block expansion past the first slice.

How FISTA Solutions launches chatbots

FISTA Solutions launches chatbots against this checklist: intent-scoped specifications from real question data, curated and permission-aware knowledge, grounded answers with citations and refusal, guardrails enforced outside the model, escalation with context, golden-set and adversarial evaluation in CI, privacy and transparency by design, analytics and quality sampling from day one, and staged rollout with owners and runbooks. The AI enablement practice delivers the retrieval and gateway platform, AI agents extend bots into action, and forward deployed engineers run the launch with your support and content teams. The record behind the approach is 150+ projects with 99.9% uptime.

To run a launch review on a chatbot, message FISTA on WhatsApp, or read chatbot vs ai agent to decide whether you need the next level of capability.