Production-Ready AI Evaluation Pipeline Build (10-15 days)
A fixed-scope engagement that instruments a client's LLM or agent deployment with tracing, scoring, and drift detection so the agency can hand over a system that is monitored, not merely shipped. It converts an unverifiable AI pilot into a retainer-backed production asset. Time: 10-15 days.
By InnovaAI ResearchPublished
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Production-Ready AI Evaluation Pipeline Build (10-15 days)
A fixed-scope engagement that instruments a client's LLM or agent deployment with tracing, scoring, and drift detection so the agency can hand over a system that is monitored, not merely shipped. It converts an unverifiable AI pilot into a retainer-backed production asset.
- Client has at least one LLM or agent workflow already running in staging or production with real user traffic or a representative test set. Access to the application repository and the ability to add SDK-level instrumentation. A named client-side owner for quality standards (product, QA, or ops lead). Agreement on 3-5 quality dimensions that matter commercially, such as factual accuracy, tone, task completion, or escalation correctness. Budget approval for platform licensing plus the agency build fee.
- 1.Inventory every LLM call, tool invocation, and retrieval step in the client workflow
- 2.Map where outputs reach a customer or a client-facing system
- 3.Confirm the quality dimensions the client will be judged on
- 1.Select the observability platform against data residency, volume, and pricing constraints
- 2.Define the trace schema: session, span, and user identifiers
- 3.Document the instrumentation plan for the engineering team
- 1.Instrument the primary workflow with the chosen platform's SDK
- 2.Capture prompt, response, latency, and token cost per call
- 3.Verify traces land correctly in the dashboard
- 1.Build a golden dataset of 50-200 representative inputs from real traffic
- 2.Label expected outputs or scoring criteria with the client owner
- 3.Store the dataset in version control alongside the prompt
- 1.Configure automated scoring for each agreed quality dimension
- 2.Set up LLM-as-judge rubrics where human labeling is too slow
- 3.Run the first evaluation pass and record baseline scores
- 1.Add cost and latency dashboards broken out by workflow stage
- 2.Define alert thresholds for spend spikes and p95 latency
- 3.Wire alerts to the client's existing on-call channel
- 1.Establish a drift detection baseline on input distribution and output scores
- 2.Configure weekly regression runs against the golden dataset
- 3.Document what triggers a rollback versus a prompt revision
- 1.Run an adversarial pass on the workflow using red-team prompts
- 2.Log failure modes by category: hallucination, refusal, leakage, loop
- 3.Prioritize the top three failure modes by client impact
- 1.Patch the highest-impact failure modes in prompt or retrieval logic
- 2.Re-run the evaluation suite and compare against baseline
- 3.Record the delta for the client readout
- 1.Write the runbook covering alert response, rollback, and escalation
- 2.Train the client owner on reading dashboards and re-running evals
- 3.Hand over credentials and dataset ownership
- 1.Deliver the final readout with baseline versus post-fix scores
- 2.Present the monthly monitoring retainer scope and price
- 3.Agree the review cadence for the next 90 days
The build fee is priced against the cost of an unmonitored failure: a single hallucinated client-facing response or a runaway token bill can exceed the entire engagement cost. Because the pipeline produces a permanent artifact (traces, golden dataset, scoring rubrics, runbook), the agency can attach a monthly monitoring retainer that renews without new scoping. Agencies that embed this early also win the follow-on work when the client expands to additional agents, since the evaluation harness is already in place.
- Instrumented workflow with full trace coverage across every LLM call and tool invocation
- Versioned golden dataset of 50-200 labeled inputs with scoring rubrics
- Cost, latency, and quality dashboard with alert thresholds configured
- Drift detection baseline and weekly regression run schedule
- Operational runbook covering alert response, rollback, and escalation paths
The client's workflow produces traces for 100% of production calls, the evaluation suite runs on a schedule and reports scores against the golden dataset, and the client owner has independently re-run an evaluation and responded to a test alert without agency assistance.