Grounded Answer Layer Build (10-14 days)
A fixed-scope engagement that stands up a retrieval layer behind a client's AI answers, with source citations, a swap-ready evaluation harness, and a cost model the retainer can defend. Time: 10-14 days.
By InnovaAI ResearchPublished
How do you implement it?
Grounded Answer Layer Build (10-14 days)
A fixed-scope engagement that stands up a retrieval layer behind a client's AI answers, with source citations, a swap-ready evaluation harness, and a cost model the retainer can defend.
- A client use case with a named document corpus (contracts, policy PDFs, product docs, or support tickets) and a written accuracy bar for answers. Access to the source systems or an export path for the corpus. One engineer or technical lead available for 4 to 6 hours per day across the build window. A staging environment where the retrieval layer can run without touching production client data. Agreement on which model provider handles generation, since retrieval quality and generation quality are measured separately.
- 1.Scope the corpus: count documents, formats, and update frequency
- 2.Write the accuracy bar as testable questions with known correct answers
- 3.Confirm data residency and retention constraints with the client
- 1.Inventory ingestion paths (connectors, exports, APIs) and pick the lowest-friction route
- 2.Document parsing gaps for scanned PDFs, images, and audio files
- 3.Flag documents that must be excluded for legal or confidentiality reasons
- 1.Stand up the chosen platform in staging and load a 10 percent corpus sample
- 2.Test chunking behavior against long documents with tables and headers
- 3.Record parse failures and entity extraction misses in a defect log
- 1.Build the evaluation harness: 30 to 50 questions scored for answer correctness and citation accuracy
- 2.Run the first baseline against the sample corpus
- 3.Capture latency and per-query cost at the current configuration
- 1.Tune retrieval settings (index mix, top-k, reranking) against the harness
- 2.Compare at least two retrieval configurations on the same question set
- 3.Freeze the winning configuration and note the margin over the runner-up
- 1.Load the full corpus and re-run the harness
- 2.Measure ingestion time and storage footprint at full scale
- 3.Identify documents that degrade answer quality and quarantine them
- 1.Wire the retrieval layer into the client's application or agent workflow
- 2.Add citation rendering so every answer shows its source document and passage
- 3.Test failure modes: empty results, stale documents, and conflicting sources
- 1.Run a blind review with two client-side reviewers scoring 20 answers
- 2.Log disagreements between reviewer scores and harness scores
- 3.Adjust the accuracy bar if the client's standard differs from the written one
- 1.Build the provider swap test: re-run the harness against a second retrieval engine
- 2.Document the migration effort in hours, not adjectives
- 3.Record pricing differences per 1,000 queries between providers
- 1.Write the cost model tying query volume to monthly platform spend
- 2.Set alert thresholds for accuracy drift and ingestion failures
- 3.Draft the runbook for adding new document sources
- 1.Hand over the evaluation harness and defect log to the client team
- 2.Walk through the swap procedure with the engineer who will own it
- 3.Confirm the retainer scope for ongoing accuracy monitoring
- 1.Run the final acceptance test against the frozen accuracy bar
- 2.Deliver the baseline report with citation accuracy and cost per query
- 3.Close out open defects or move them to a named owner with dates
The build is priced against the alternative: a client hiring an in-house engineer for six to ten weeks to assemble parsing, chunking, and semantic search from scratch. Because the retrieval layer sits behind an evaluation harness the agency owns, the same harness can be re-run for every new client corpus, so the second engagement costs roughly 40 percent of the first. Ongoing accuracy monitoring converts a one-time build into a retainer, and the swap test gives the agency a defensible answer when a client asks why the platform choice was made.
- A working retrieval layer in the client's staging environment with source citations on every answer
- An evaluation harness of 30 to 50 scored questions that any retrieval provider can be tested against
- A provider swap report documenting migration hours and per-query pricing differences
- A cost model mapping monthly query volume to platform spend with alert thresholds
- An ingestion runbook covering new sources, parse failures, and document quarantine rules
The client's application returns cited answers that meet the written accuracy bar on the frozen question set, and a second retrieval provider has been tested against the same harness with migration effort documented in hours.