Grounded Answer Layer for Client AI Apps (10-14 days)
A productized engagement that stands up a retrieval layer behind a client's AI assistant so every answer cites a source document, then wraps it in an evaluation harness the agency owns. The client gets grounded outputs; the agency keeps the swap rights on the retrieval engine. Time: 10-14 days.
By InnovaAI ResearchPublished
How do you implement it?
Grounded Answer Layer for Client AI Apps (10-14 days)
A productized engagement that stands up a retrieval layer behind a client's AI assistant so every answer cites a source document, then wraps it in an evaluation harness the agency owns. The client gets grounded outputs; the agency keeps the swap rights on the retrieval engine.
- A named client AI use case with a measurable error cost (support deflection, proposal drafting, underwriting review) and an executive sponsor who owns that number. At least 200 representative source documents or records available in a readable format, plus written permission to process them. A staging environment where the retrieval layer can run without touching production traffic. Agreement on the accuracy threshold that counts as pass, expressed as a percentage on a fixed question set. One engineer or technical lead allocated for the full window, not borrowed part time.
- 1.Run a scoping session to pin the single highest-cost question type the client wants answered
- 2.Inventory source systems (drive folders, ticketing exports, contract repositories) and count usable documents
- 3.Record the current baseline: how often staff answer that question today, and at what cost per answer
- 1.Build a 50-question gold set with the client's subject matter expert, including 10 questions the current process gets wrong
- 2.Define the pass criteria in writing: correct answer plus a citation that resolves to a real source passage
- 3.Agree on the scoring rubric so pass or fail is not a judgment call later
- 1.Connect the chosen platform to the source systems and run a full ingestion pass
- 2.Log parse failures, scanned PDFs, and files that need OCR or manual cleanup
- 3.Report the usable-document percentage before any tuning begins
- 1.Tune chunking and metadata filters against the gold set rather than defaults
- 2.Test hybrid retrieval (keyword plus vector) on the 10 known-hard questions
- 3.Capture a first accuracy number and the failure list
- 1.Wire the retrieval layer into the client's application or agent through the platform's API
- 2.Enforce citation rendering in the response format so no answer ships without a source link
- 3.Add a fallback path that returns 'not found in sources' instead of a confident guess
- 1.Stand up the agency-owned evaluation harness that scores every run against the gold set
- 2.Automate the scoring so a provider swap can be measured in hours, not weeks
- 3.Store run history with timestamps for client-facing reporting
- 1.Run the gold set against a second retrieval provider behind the same interface
- 2.Compare accuracy, latency, and cost per thousand queries side by side
- 3.Document where each engine wins so the recommendation is evidence-based
- 1.Harden access controls: per-user permissions, audit logging, and a verified deletion path for client data
- 2.Confirm the retrieval layer respects document-level permissions rather than indexing everything for everyone
- 3.Walk the client's security reviewer through the data flow
- 1.Tune for cost: cache frequent queries, cap context size, and route simple lookups to cheaper models
- 2.Measure cost per answered question at projected monthly volume
- 3.Set a spend alert threshold in the client's billing account
- 1.Run a two-week simulation of real query volume against the staging build
- 2.Log every answer that a reviewer would have rejected and classify the cause
- 3.Fix the top three recurring failure classes
- 1.Train the client's team on the evaluation harness and the swap procedure
- 2.Hand over the runbook covering reindexing, adding sources, and rolling back a provider change
- 3.Rehearse a provider swap live with the client's engineer driving
- 1.Present final accuracy, latency, and cost numbers against the day-one baseline
- 2.Deliver the provider comparison memo with a written recommendation and a named alternative
- 3.Agree the monthly monitoring retainer scope and the accuracy floor that triggers a review
The billable hours sit in ingestion cleanup, gold-set construction, and evaluation harness work, none of which the client can buy off the shelf, so the agency prices against the cost of the wrong answer rather than against API list pricing. Because the harness belongs to the agency, the monthly retainer covers accuracy monitoring and periodic provider benchmarking, which is recurring revenue tied to a number the client already tracks. A provider swap takes hours once the harness exists, so the agency keeps margin even when retrieval pricing falls, instead of watching a fixed markup erode.
- A 50-question gold set with scoring rubric and recorded baseline accuracy
- A working retrieval layer behind the client's application with enforced source citations
- An agency-owned evaluation harness that scores any provider against the gold set on demand
- A provider comparison memo covering accuracy, latency, and cost per thousand queries
- An operations runbook covering reindexing, permission changes, and provider rollback
The client's assistant answers the gold set at or above the agreed accuracy threshold with a resolvable citation on every response, and the agency's harness can score a replacement retrieval provider end to end without code changes to the client application.