AI System Evaluation
AI doesn't fail like software. It fails confidently wrong.
Traditional software either works or it doesn't. AI produces probabilistic outputs — a distribution of responses, some right, some wrong, some confidently wrong. Testing it requires a different discipline. We build the evaluation pipelines that catch it before your clients do.
The problem with AI quality
A wrong answer isn't a binary failure.
Traditional software either works or it doesn't. AI is different — it produces probabilistic outputs. A wrong answer isn't a binary failure; it's a distribution of responses, some right, some wrong, some confidently wrong.
Testing AI requires a different discipline. You can't rely on a deterministic pass/fail; you have to measure behavior against ground truth, score it on every change, and catch the failure modes that only AI systems exhibit.
What can go wrong
The failure modes only AI systems exhibit.
Each one is invisible to traditional QA — and each one is testable if you build the right pipeline.
Hallucination
Invented factsThe model invents facts not in its training data or the provided context — and states them with full confidence.
Context / RAG failure
Wrong retrievalThe RAG pipeline retrieves the wrong document chunks; the LLM answers from irrelevant context.
Prompt injection
Broken behaviorA user's input breaks the intended behavior of the system.
Drift
Silent changeModel or embedding updates change system behavior — and you don't notice until a client complains.
Compliance violation
Legal exposureThe AI outputs something that violates TCPA, HIPAA, FTC guidelines, or your client's legal constraints.
Latency regression
SLA breachA change in the pipeline increases response time beyond an acceptable SLA.
Automated evaluation frameworks
Score correctness on every change.
We build a golden dataset and run automated scoring on every code or model change — so you know whether the system still answers correctly before it ships.
Golden dataset
Curated question/answer pairs that represent correct system behavior — the ground truth every change is measured against.
Scoring on every change
Automated evaluation runs on every code or model change: does the system still answer correctly?
The metrics that matter
Recall (did the right chunks get retrieved?), precision (were irrelevant chunks included?), answer correctness (does the final answer match ground truth?), and hallucination rate.
Tooling
RAGAS, DeepEval, LangSmith, Weights & Biases, and custom eval scripts — wired into your pipeline.
RAG pipeline evaluation
Validate that the right context reaches the model.
Retrieval is where most RAG systems quietly break. We evaluate every layer of the pipeline against your data.
Retrieval evaluation
Does the vector search surface the right documents for a given query?
Chunking strategy testing
Does the chunking approach preserve context for multi-step reasoning?
Reranker evaluation
Does the reranking layer improve precision over raw retrieval?
Context-window utilization
Is the LLM receiving too much or too little context?
Embedding-model comparison
Which embedding model produces the best retrieval accuracy for this data type?
Prompt regression testing
Every prompt change is a potential regression.
We treat prompts like code — versioned, tested against ground truth, and reversible.
Prompt version control — track every prompt change with a hash.
A/B test harness for prompt variants against the golden dataset.
Automated alert if a prompt change degrades accuracy beyond your threshold.
Rollback mechanism — revert to the previous prompt version if a regression is detected.
LLM output compliance testing
For regulated industries, what the AI won't say matters.
For automotive, healthcare, financial services, and legal — we build the test suites and red-teaming that keep outputs inside the lines.
Compliance test suite
Inputs that should never produce certain outputs — codified as a repeatable test suite.
Automated red-teaming
Adversarial inputs designed to elicit policy violations, run automatically against every change.
Output-filter validation
Test that guardrails actually block what they're supposed to block.
Domain-specific testing
TCPA compliance for automotive comms, PHI disclosure prevention for healthcare, and investment-advice boundaries for financial services.
Human-in-the-loop QA
Where automated evaluation hasn't caught up yet.
Some AI outputs require human review before automated evaluation catches up — so we build the sampling and feedback loop around it.
Structured sampling
Structured sampling of production AI outputs for human scoring on a weekly or monthly cadence.
Annotator guidelines
Annotator guidelines and inter-rater reliability scoring so human judgments stay consistent.
Feedback loop
Human corrections feed back into fine-tuning data or prompt improvement.
Production drift monitoring
Statistical sampling of live responses to catch drift before a client does.