Production AI Readiness Audit and Eval Harness Build (10-15 days)
A fixed-scope engagement that instruments a client's live LLM feature with tracing, scoring, and drift alerts, then hands over a scored baseline the client's team can defend in a board or procurement review. It converts an unmonitored AI deployment into a documented, retainer-ready production system. Time: 10-15 days.
By InnovaAI ResearchPublished
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Production AI Readiness Audit and Eval Harness Build (10-15 days)
A fixed-scope engagement that instruments a client's live LLM feature with tracing, scoring, and drift alerts, then hands over a scored baseline the client's team can defend in a board or procurement review. It converts an unmonitored AI deployment into a documented, retainer-ready production system.
- Client grants read access to the LLM application repository and its deployment environment
- A named client-side owner (engineering lead or product manager) available for two 45-minute checkpoints per week
- At least 200 real production interactions or a synthetic scenario set of comparable size available for scoring
- Written agreement on which quality dimensions matter commercially (accuracy, tone, latency, cost per session)
- Budget approval for platform licensing plus any human rater hours if voice or emotional-tone scoring is in scope
- 1.Inventory every LLM call path in the client application, including retrieval steps and tool invocations
- 2.Document current failure reporting: what the client learns today and how fast they learn it
- 3.Agree the three quality dimensions that map to client revenue or retention
- 1.Select the observability platform against integration fit, data residency, and pricing model
- 2.Map the trace schema: spans, sessions, user identifiers, and cost attribution fields
- 3.Confirm retention window and who inside the agency and client can query raw traces
- 1.Instrument the primary call path with the chosen platform's SDK
- 2.Capture prompt, response, latency, token count, and model version on every span
- 3.Verify traces land in the dashboard with correct session grouping
- 1.Extend instrumentation to secondary paths: fallback models, retries, and background jobs
- 2.Tag traces by client tenant or product surface where multi-tenant
- 3.Run a load check to confirm instrumentation does not add measurable latency
- 1.Define the scoring rubric for each agreed quality dimension
- 2.Build automated scorers for the deterministic checks (format validity, refusal rate, citation presence)
- 3.Configure an LLM-as-judge scorer for the subjective dimensions and calibrate it against 30 hand-labeled examples
- 1.Assemble the evaluation dataset from production traces plus edge cases the client names
- 2.Split into a regression set and a held-out set
- 3.Record the baseline scores that will anchor every future release decision
- 1.Wire the evaluation suite into the client's CI or pre-release checklist
- 2.Set pass thresholds per dimension and define what triggers a release block
- 3.Document the rollback path when a threshold fails
- 1.Configure drift and anomaly alerts on score movement, latency percentiles, and cost per session
- 2.Route alerts to the client's on-call channel and the agency's delivery lead
- 3.Test each alert by injecting a deliberately degraded response
- 1.Run a red-team pass against the agent's guardrails using adversarial prompts
- 2.Log every jailbreak attempt and its outcome as a scored trace
- 3.Rank findings by client-facing risk and remediation effort
- 1.Review the first week of live traces with the client owner
- 2.Identify the top three failure patterns by frequency and cost impact
- 3.Draft the remediation backlog with effort estimates
- 1.Write the runbook: how to read a trace, how to triage an alert, how to re-run the eval suite
- 2.Record a 20-minute walkthrough for the client's team
- 3.Hand over dashboard credentials and document access tiers
- 1.Deliver the scored baseline report with before-and-after failure visibility
- 2.Present the retainer proposal covering ongoing eval maintenance and monthly quality reviews
- 3.Close with a 30-day check-in date and named owners on both sides
The agency bills for diagnostic and build labor that the client cannot staff internally, then converts the engagement into a monthly eval-maintenance retainer priced at 15-25% of the original build. Because the harness produces a defensible quality baseline, the client's procurement and legal teams stop blocking further AI spend, which shortens the next project's sales cycle and raises average contract value across the account.
- Instrumented trace pipeline covering every production LLM call path
- Scored evaluation suite with a documented baseline and pass thresholds wired into release checks
- Drift, latency, and cost alert configuration with tested routing
- Red-team findings register ranked by client-facing risk
- Operations runbook plus a recorded handover walkthrough
The client's team can independently open a production trace, read its scores, and explain why a release passed or failed the eval suite without agency assistance.