Grounded Answer Layer for Client AI Products (10-15 days)
A productized engagement that stands up a retrieval layer behind a client's AI feature so answers cite source documents instead of model memory, with a provider-swap harness the agency owns. It turns document ingestion, chunking, and semantic search plumbing into a fixed-scope delivery rather than an open-ended engineering retainer. Time: 10-15 days.
By InnovaAI ResearchPublished
How do you implement it?
Grounded Answer Layer for Client AI Products (10-15 days)
A productized engagement that stands up a retrieval layer behind a client's AI feature so answers cite source documents instead of model memory, with a provider-swap harness the agency owns. It turns document ingestion, chunking, and semantic search plumbing into a fixed-scope delivery rather than an open-ended engineering retainer.
- A client AI feature or support workflow already in production that returns uncited answers, plus a named business owner who can approve scope. At least one document corpus of 200 or more files in a consistent format, with a sample export available before kickoff. Written client agreement that the agency owns the evaluation harness and prompt layer, while the retrieval vendor remains a swappable component. Access to a staging environment and a budget line for retrieval API usage during the build window. A baseline accuracy score captured on the current ungrounded output so improvement can be measured rather than asserted.
- 1.Inventory the client's source documents by format, volume, and update frequency
- 2.Interview the business owner on which questions the AI feature must answer correctly
- 3.Capture a baseline accuracy score on 30 real user queries against the current output
- 1.Define the retrieval evaluation set: 50 queries with expected source passages
- 2.Agree on the accuracy threshold that counts as a pass for this client
- 3.Document the fallback behavior when no source clears the threshold
- 1.Stand up the chosen platform's ingestion pipeline against a staging corpus
- 2.Configure parsing and chunking rules for the client's dominant document types
- 3.Log ingestion failures and malformed documents for the client's data owner
- 1.Build the entity extraction pass for names, dates, and contract identifiers
- 2.Verify extracted entities against a manual sample of 20 documents
- 3.Record extraction precision so the client sees where grounding is weakest
- 1.Wire retrieval into the client's prompt layer with source citations attached
- 2.Test three retrieval modes (vector, keyword, hybrid) against the evaluation set
- 3.Pick the mode that clears the accuracy threshold at the lowest token cost
- 1.Run the full evaluation set and score citation correctness per answer
- 2.Isolate the ten worst queries and diagnose whether retrieval or prompting failed
- 3.Tune chunk size and top-k until the pass rate stabilizes
- 1.Build the provider-swap harness: one interface, two retrieval backends
- 2.Re-run the evaluation set against the second backend to prove portability
- 3.Document the swap procedure in under one page for the client's engineers
- 1.Add a citation audit log that records which source supported each answer
- 2.Connect the log to the client's existing analytics or admin console
- 3.Confirm the client can trace any answer back to a document without agency help
- 1.Stress test with the client's largest and messiest document set
- 2.Measure latency and per-query cost at expected production volume
- 3.Flag any document class that consistently fails retrieval
- 1.Run a live acceptance session with the client's business owner
- 2.Score the final pass rate against the day-two threshold
- 3.Hand over the evaluation harness and the swap runbook
- 1.Deliver the accuracy report with before and after scores
- 2.Present the monthly cost model at three volume tiers
- 3.Agree the retainer scope for ongoing evaluation and re-indexing
The agency charges for an accuracy outcome rather than for engineering hours, because the client is buying cited answers, not an integration. Margin holds when the evaluation harness is reusable across accounts: the second client in the same vertical reuses the query set, chunking rules, and swap runbook, cutting delivery time by roughly a third. The retainer is defensible because retrieval quality drifts as client documents change, and only the agency holds the benchmark that detects the drift.
- Retrieval evaluation harness with 50 scored queries and a pass threshold
- Provider-swap runbook covering two interchangeable retrieval backends
- Citation audit log connected to the client's analytics or admin console
- Accuracy report showing baseline versus post-build pass rates
- Monthly cost model at three production volume tiers
The client's AI feature answers the agreed evaluation set at or above the day-two accuracy threshold, every answer carries a traceable source citation, and the client's own engineer has executed one provider swap using the runbook without agency assistance.