How we keep AI from inventing offers when your catalog is still loading.
The fastest way to embarrass a new AI rollout is to let it answer pricing, packaging, or compliance questions before your own documents are indexed. Users ask the hardest question first. If the system improvises, you lose credibility before you ever tune a prompt.
Evidence-first onboarding is our default playbook: connect sources of truth, verify retrieval, then widen what the copilot is allowed to say in customer-facing channels.
Step one: sources, not prompts
We start integrations with read-only access to approved folders, wikis, or CMS exports. The goal is not completeness on day one; it is correctness for what is connected. Empty sections are better than confident wrong answers.
During onboarding we surface coverage gaps explicitly: topics users ask about where no snippet met the retrieval threshold. That list becomes your documentation backlog, not a mystery buried in logs.
Step two: narrow the blast radius
Until coverage looks healthy, we keep customer-facing generation in suggest-only mode or route it through reviewers. Internal summarization can be more permissive because the cost of a mistake is lower and the reader knows to verify.
This sequencing feels slower than "turn everything on." It is faster than rebuilding trust after a prospect receives a made-up discount policy.
Dreams versus docs
Large language models are great at fluency. They are not a substitute for your catalog, your legal approvals, or your regional exceptions. CRAIM is opinionated: the copilot should cite or silently abstain—not guess—when the evidence is missing.
That is docs before dreams. Once the evidence is in place, the same model becomes dramatically more useful because it finally has something solid to condition on.