Pre-Pilot: Scope and Compliance Setup
A 2-week fixed-scope pilot in a German insurance firm must produce a working RAG assistant on one workflow, a GDPR-compliant data-flow document, and a measured before/after baseline. The checklist below is operational: each item is a task a team can mark done or not done. It assumes the team uses LangChain and LangGraph, integrates with Slack or Microsoft Teams, and targets lead qualification to cut first-response time. The pilot is not a production deployment; it is a scoped experiment with a clear exit criterion. Work through the items in order. Skipping the audit or the baseline measurement invalidates the pilot’s value as a decision input for rollout.
Build the RAG Pipeline on LangChain and LangGraph
The RAG pipeline is the core of the pilot. Build it on LangChain for document chunking, embedding, and vector search, and on LangGraph for the stateful workflow that routes queries, handles multi-turn context, and triggers the human-approval gate. Keep the graph simple: one retrieval node, one generation node, one approval gate. Use a managed vector store in an EU region for the pilot. If the client’s data cannot leave the building, switch to an on-premises vector store and an open-weight model on the client’s GPU hardware. The RAG code is identical; only the embedding and inference endpoints change. Test the pipeline against 20 real lead queries before integrating with Slack or Teams.
Integrate with Slack or Microsoft Teams
The pilot must integrate with the channel the team already uses: Slack or Microsoft Teams. Build a bot that receives the lead query, calls the RAG pipeline, and returns the draft qualification score and suggested next step. The bot must include a human-approval gate: if the AI’s confidence drops below a threshold, or if the lead involves health-related data, the bot flags the query for a human agent. Log every human override. The integration must not replace the existing CRM or helpdesk; it plugs into them via their APIs. For a 2-week pilot, use a single OpenAI or Anthropic API endpoint for the LLM layer. Keep the model-agnostic layer thin: a single abstraction over the API call so switching providers later requires only a config change.
Measure the Before/After Baseline
Before the pilot starts, measure the baseline: cycle time from lead entry to qualified status, and error rate (misclassified leads) for the 2 weeks prior. Document the sample size, the definition of ‘error,’ and the measurement method. During the pilot, measure the same metrics for the 2 weeks of the pilot. The before/after comparison is the pilot’s primary deliverable. Without it, the client has no objective basis for the rollout decision. The baseline report must include: the number of leads processed, the average cycle time before and after, the error rate before and after, and the number of human overrides. This report is the exit criterion for the pilot.
Define the Pilot Exit Criterion
The pilot is a fixed-scope engagement: the vendor delivers a defined set of artifacts within the 2-week deadline. It is not a subscription or managed service. After the pilot, the client decides whether to proceed to rollout. The pilot includes a measured before/after comparison on cycle time and error rate, giving the client objective data to justify or reject the full deployment. The exit criterion is clear: if the pilot reduces cycle time by at least 30% and error rate by at least 20%, the client proceeds to rollout. If not, the pilot ends, and the client retains the baseline report and the RAG pipeline code. The vendor does not retain any client data after the pilot ends.
Maintain the Checklist Over Time
The checklist is a living document. After the pilot, review each item: mark what worked, what did not, and what needs adjustment. If the pilot proceeds to rollout, update the checklist to reflect the new scope: additional workflows, multi-language support, production monitoring. If the pilot ends, archive the checklist with the baseline report. Revisit the checklist before any new pilot: the GDPR landscape, the LLM provider landscape, and the integration landscape change. The checklist is not a one-time artifact; it is a tool for continuous improvement in AI-native operations. Keep it in the team’s project management tool, not in a static PDF.
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