AI evaluation and QA
Evaluation systems and quality engineering for AI products, agents, retrieval systems, and production releases.
Who this is for
This is for you if you cannot tell whether a model or prompt change improved the product, or release quality still depends on informal checks.
What it is
AI evaluation and QA makes product behavior measurable before and after release. It combines task-specific evaluation, software testing, calibrated graders, human review, and production signals to find regressions and improve decisions.
Problems we work on
- A team cannot tell whether a model or prompt change improved the product
- Agent and retrieval failures appear only after users encounter them
- Release quality depends on informal manual checks
- Production traces are not feeding a useful evaluation set
- A headline score hides severe failures in important slices
The work
- Define task-level quality criteria and representative test cases
- Build datasets, harnesses, graders, and regression checks
- Combine deterministic, model-based, and human evaluation
- Inspect trajectories, tool use, outcomes, cost, and latency
- Use production failures to expand coverage and improve release gates
What an error rate means
See how a small error rate turns into real weekly mistakes at your volume.
Left alone, that is 12,990 in a year. With evaluation finding and fixing failures every month, it is 5,041 — and falling.
Your numbers, not a measurement. The model assumes evaluation cuts the error rate 20% each month as failures are found and fixed; real rates vary by task and are exactly what an evaluation suite exists to measure.
How the engagement runs
- A small team is composed around the work instead of a fixed staffing shape
- The engagement runs on a monthly cadence with named outputs and exit criteria
- It ends with a documented handoff to your team or continued operation
How Aravali Labs works
- Measure the user task and system outcome rather than only model scores
- Separate capability tests from near-perfect regression gates
- Calibrate model graders against structured human judgment
- Review failures by category, severity, slice, and business effect
- Document harness, model, prompt, tool, data, and scoring versions
Outputs
- A versioned golden set and taxonomy of production failure modes
- Deterministic checks, model graders, and human-review protocols
- Release gates tied to severity and regression policy
- Trace-level diagnostics for agents, tools, and retrieval
- A loop that turns production incidents into new test cases
What to measure
- Task success, regression pass rate, and critical failures
- Performance by customer, workflow, language, and difficulty slice
- Grader agreement, calibration drift, and false-pass rate
- Evaluation coverage, execution cost, and time to decision
- Production incidents that were and were not represented before release
Questions
- How is AI evaluation different from software testing?
- Software tests check deterministic behavior. AI evaluation also measures variable model outputs and multi-step trajectories against task-specific criteria and human judgment where needed.
- Can evaluation run as part of product delivery?
- Yes. Evaluation can run during development, on pull requests, before release, after model or prompt changes, and against sampled production behavior.
- Is a single aggregate score enough?
- No. Release decisions should also inspect critical failures, regression gates, grader calibration, and performance across important slices.