Grounding, evaluation and graceful failure: how to add AI assistants and semantic search to business software without eroding confidence.
AI features fail in production for boring reasons: ungrounded answers, no evaluation harness and no fallback when the model is unavailable.
We ground assistants in your own data with retrieval, cite sources inline, and constrain the assistant to actions it is allowed to perform. Every release runs against a fixed evaluation set before it ships.
Trust compounds. A narrow assistant that is right every time beats a broad one that is right most of the time.