Score-only pipelines
A failed grade without an approved replacement does not change behavior.
LLM evaluation
LLM evaluation services that turn failures into reusable corrections.
Scores tell you what broke. We help you capture the response that should have happened—and test whether retrieving it improves the next similar request.
Evaluation that stops at diagnosis is incomplete.
Dashboards and frameworks flag regressions. They rarely give the agent a better example to follow next time.
Prompt sprawl
Packing every exception into the system prompt eventually weakens what already works.
Offline benches only
Lab sets miss production tools, policies, and messy user language.
No correction memory
Reviewer fixes stay in tickets instead of becoming eval-ready experiences.
Evaluation plus an experience layer
We connect LLM evaluation to retrieval-augmented human feedback: measure, correct, store outside weights, retrieve, re-measure.
Production-grounded eval
Reconstruct traces and define success from your workflow—not a generic leaderboard.
Human-approved replacements
Capture the trajectory or response that should replace the failure.
Before/after evidence
Compare task success, edits, escalations, latency, and cost on one focused flow.
What an evaluation service should cover
An evaluation engagement must answer more than whether a model passed. It should establish what counts as success, how regressions are caught, and which evidence changes a release decision.
Evaluation design
Define task-level success criteria, representative scenarios, and the mix of automated graders and human review needed for the workflow.
Regression workflow
Version scenarios, prompt/tool changes, and expected outputs so a release can be compared to a stable baseline.
Production feedback loop
Bring real failures back into the set, then decide whether a correction belongs in retrieval, a prompt, a tool policy, or a future training run.
Build the approved examples used in evaluation with golden datasets for AI agentsApply the same workflow to call outcomes through voice AI testing services
Who this is for
- Teams shipping AI agents who need reliable, repeatable evaluation
- Support, voice, and ops workflows where mistakes are expensive
- Leaders comparing fine-tuning vs retrieval of approved examples
- Anyone tired of eval tools that stop at a red score
What the sprint includes
A — Audit
Failure and opportunity analysis with a rubric tied to your definition of success.
B — Build
Corrected experience dataset + retrieval prototype for one production flow.
C — Prove
Blinded quality review and a clear rollout recommendation.
Proof over promises
We do not promise lift before examining the workflow. The first engagement exists to produce measured evidence.
Related
Book an LLM evaluation that starts from your traces.
Bring a sample of production failures. We will map patterns and propose one Agent Improvement Sprint.