Grounded Answer Layer Build (10-18 days)
A productized engagement that stands up a retrieval and citation layer over a client's document estate, wrapped in an agency-owned evaluation harness so retrieval vendors stay swappable. Clients get source-cited AI answers; the agency keeps the accuracy benchmark as its own asset. Time: 10-18 days.
By InnovaAI ResearchPublished
How do you implement it?
Grounded Answer Layer Build (10-18 days)
A productized engagement that stands up a retrieval and citation layer over a client's document estate, wrapped in an agency-owned evaluation harness so retrieval vendors stay swappable. Clients get source-cited AI answers; the agency keeps the accuracy benchmark as its own asset.
- [Client grants read access to the source corpus (shared drives, wikis, ticketing exports) with a named data owner per system, A written list of the 10-20 questions the client expects the AI to answer correctly, Agreement on which answers carry legal or financial consequence and therefore require passage-level citation, A retrieval budget ceiling per month so vendor pricing can be compared on equal terms, One internal engineer or analyst allocated 4-6 hours per week for the build window]
- 1.Inventory every document source and record volume, file types, and refresh cadence
- 2.Interview two or three end users about the questions they actually ask today
- 3.Flag sources that contain regulated or client-confidential material
- 1.Draft the 15-question gold set with the client's subject expert, including three deliberately unanswerable questions
- 2.Define what a correct answer looks like for each item: exact passage, acceptable paraphrase, or refusal
- 3.Agree scoring rules so two reviewers reach the same verdict
- 1.Stand up two candidate retrieval providers side by side against the same corpus
- 2.Configure ingestion connectors for each source system and confirm sync status
- 3.Record ingestion time and any parsing failures per provider
- 1.Run the gold set through both providers and log retrieval hit rate and citation accuracy
- 2.Note where chunking splits tables or headers in ways that break answers
- 3.Capture per-query latency and token spend for each run
- 1.Score results against the gold set and pick a primary provider on evidence, not sales demo
- 2.Document the two weakest question categories and the cause of each miss
- 3.Decide whether a document-cleaning pass is needed before re-indexing
- 1.Build the evaluation harness as a repeatable script the agency owns, not a vendor dashboard
- 2.Wire the harness to accept a provider swap through a single configuration change
- 3.Store baseline scores in version control for future comparison
- 1.Tune chunking and metadata filters for the failing categories
- 2.Add entity extraction or keyword weighting where semantic search alone underperforms
- 3.Re-run the gold set and record the delta
- 1.Connect the retrieval layer to the client's answer surface (chat widget, internal assistant, or API)
- 2.Implement passage-level citation rendering so every claim links to its source page
- 3.Add a refusal path for questions the corpus cannot support
- 1.Instrument per-query cost and latency logging with alert thresholds
- 2.Set a monthly spend cap and a fallback behavior when the cap is reached
- 3.Test the fallback by simulating a provider outage
- 1.Run a blind review with three client stakeholders scoring 20 live answers
- 2.Log every disagreement and resolve against the gold set definitions
- 3.Fix the top three recurring failure modes
- 1.Write the provider swap runbook: re-index steps, harness command, and acceptance threshold
- 2.Document the corpus refresh schedule and who owns each connector
- 3.Hand over credentials and the evaluation baseline
- 1.Present accuracy, cost per answered query, and the swap procedure to the client sponsor
- 2.Agree the monthly re-benchmark cadence and the retainer scope around it
The build itself is a fixed-scope project a client can price against a stalled internal attempt, so the setup fee lands in the five-figure range without discounting. The margin sits in the evaluation harness: once the agency owns the gold set and scoring script, every quarterly re-benchmark is a billable retainer task that takes hours rather than weeks. It also removes the single-vendor trap, because swapping retrieval providers becomes a configuration change the agency performs and charges for, not a re-platforming project the client runs alone.
- A 15-question gold set with scoring definitions signed off by the client's subject expert
- An agency-owned evaluation harness that scores any retrieval provider against the gold set
- A provider comparison memo covering retrieval accuracy, citation fidelity, latency, and cost per query
- A provider swap runbook with re-index steps and an acceptance threshold
- A citation-rendering answer surface with a documented refusal path and spend cap
The client's answer surface returns passage-level citations on the gold set at or above the agreed accuracy threshold, and the agency can re-run the harness against a second provider and produce a scored comparison within one working day.