"Keeping the knowledge base fresh: the update pipeline nobody plans for"
Every RAG project plans the initial ingestion in loving detail and treats updates as a footnote. Then a policy changes, the index doesn't, and the assistant spends a month confidently citing the old rules. The update pipeline is not an optimisation — it is where the system's honesty lives.
Three weeks after launch, someone updates the expenses policy. The document is edited, approved, published — and your AI assistant carries on citing the old limits for a month, because nothing told the index. When a user finally notices, the question that lands in your inbox is uncomfortable precisely because it is fair: if the assistant can be this confidently out of date, why would anyone trust it on anything?
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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