A support copilot that knows when evidence is not enough.
A fictional example showing how to present hybrid retrieval, answer evaluation, abstention, and human escalation as one production system.
What made this
worth solving.
The example support team had accurate knowledge spread across long, overlapping documents. A basic vector search returned plausible passages but could not reliably recognize weak evidence or conflicting policies.
Engineering choices,
not feature lists.
- 01
Combined semantic retrieval with exact-term search, then reranked candidates against the user’s intent.
- 02
Added offline evaluation sets for retrieval quality, groundedness, citation accuracy, and safe abstention.
- 03
Introduced caching, model routing, and human escalation when evidence confidence dropped below a defined threshold.
What changed after
the system shipped.
In this fictional demo, the evaluated workflow improved answer accuracy by 27% and reduced inference cost by 38% while making unsupported answers visible to reviewers.