Production AI Readiness Audit (7-12 days)
A fixed-scope diagnostic that instruments a client's live AI feature with tracing, scoring, and drift checks, then hands over a scored reliability report the agency can bill against. It converts an unmonitored pilot into a supportable retainer line. Time: 7-12 days.
By InnovaAI ResearchPublished
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Production AI Readiness Audit (7-12 days)
A fixed-scope diagnostic that instruments a client's live AI feature with tracing, scoring, and drift checks, then hands over a scored reliability report the agency can bill against. It converts an unmonitored pilot into a supportable retainer line.
- Client grants read access to the production LLM application, its prompt library, and its API billing console At least 200 real production interactions (chat, voice, or agent sessions) exist to sample from A named client-side owner for the AI feature is available for two 45-minute review sessions Baseline monthly token and inference spend is documented before instrumentation begins Legal sign-off exists for storing redacted conversation logs in the agency's observability workspace
- 1.Inventory every LLM call path, tool invocation, and retrieval step in the client application
- 2.Map which outputs reach end users versus internal staff
- 3.Log current monthly inference spend and latency percentiles
- 1.Select the tracing platform against data residency, self-hosting, and budget constraints
- 2.Define the trace schema: session, span, and user identifiers
- 3.Confirm retention window and redaction rules with the client owner
- 1.Instrument the application with the chosen SDK and verify spans arrive intact
- 2.Backfill 30 days of historical logs where the platform supports import
- 3.Validate that cost and latency dashboards populate correctly
- 1.Write the first scoring rubric covering accuracy, tone, and task completion
- 2.Calibrate the automated judge against 50 human-labeled examples
- 3.Record inter-rater agreement so the client sees the rubric is defensible
- 1.Run the scoring pass across the full production sample
- 2.Segment failures by prompt version, model, and user cohort
- 3.Quantify the failure rate in client-facing terms, not model metrics
- 1.Build the drift baseline for input distribution and output quality
- 2.Set alert thresholds for cost spikes, latency regressions, and refusal rates
- 3.Test each alert by injecting a synthetic failure
- 1.Draft the reliability report with scored findings and ranked root causes
- 2.Attach three representative failing traces as evidence
- 3.Price the remediation backlog as a separate statement of work
- 1.Walk the client owner through findings and confirm severity rankings
- 2.Agree which fixes ship inside the audit and which become follow-on work
- 3.Capture the client's own quality bar in writing for future scoring runs
- 1.Ship the two highest-severity prompt or retrieval fixes
- 2.Re-run the scoring pass and measure movement against baseline
- 3.Document the before-and-after delta for the client's internal stakeholders
- 1.Hand over dashboard access, alert routing, and runbook documentation
- 2.Train one client staffer to read traces and triage alerts
- 3.Schedule the 30-day regression review as a retainer touchpoint
Agencies can charge a premium because the audit produces a defensible quality number the client cannot generate internally, and that number becomes the justification for an ongoing monitoring retainer. The margin sits in the second and third months: once traces and scoring rubrics exist, each monthly review costs a few hours of analyst time while billing at a fixed retainer rate. Clients who have already absorbed one production incident rarely push back on the fee, because the alternative is discovering the failure in front of their own customers.
- Instrumented tracing pipeline with cost, latency, and quality dashboards Scoring rubric with documented calibration against human labels Reliability report ranking failure modes by client-facing severity Drift and cost alert configuration with a tested runbook Remediation backlog priced as a follow-on statement of work
The client's production AI feature emits complete traces, scores against a calibrated rubric, and fires a verified alert on an injected failure, with all dashboards accessible to a named client owner.