Prove your LLM workflow is actually working.
We audit existing AI systems against your data and your definition of good — then fix the top failures and gate future changes on evals in CI.
What we measure.
Six evaluation areas, each with a concrete check list. We run what fits your system and skip what doesn’t.
Retrieval Quality
Measure whether the right context reaches the model. We test retrieval in isolation so you know if bad answers are a retrieval problem or a generation problem.
- Hit rate @ k on curated question sets
- Recall vs. a labeled gold set
- Reranker uplift analysis
- Permission-aware filtering correctness
- Chunking and embedding model comparison
Answer Faithfulness & Hallucination
Grade whether answers are supported by retrieved context. Separate fabricated facts from unsupported-but-plausible claims.
- Claim-level faithfulness scoring
- Hallucination rate by topic and document source
- Citation coverage and correctness
- Refusal behavior on out-of-scope queries
Task Success for Agents
For agentic workflows, measure end-to-end completion, not just token-level quality.
- End-to-end task success rate on gold tasks
- Tool-call correctness and argument validation
- Recovery behavior on tool failures
- Step efficiency and cost per successful task
Cost, Latency, and Reliability
Production-readiness metrics that decide whether a system survives real traffic.
- p50 / p95 latency per route and model
- Token and dollar cost per request, per user, per tenant
- Error rate and retry behavior
- Model routing and fallback correctness
Safety, PII, and Policy
Detect what your system should never do, and prove it doesn't do it.
- PII leakage on input and output
- Prompt-injection resistance on untrusted content
- Jailbreak and policy-violation rate
- Sensitive topic handling per vertical (health, legal, finance)
Regression & Drift
AI systems change when you change prompts, models, or data. Catch regressions before users do.
- Prompt version A/B eval on frozen test sets
- Model upgrade regression sweeps
- Data drift alarms on production traffic
- CI eval gates on pull requests
Versioned, diffable, gated on CI.
Your eval battery lives in your repo next to your prompts. Every PR runs it. Regressions get blocked, not discovered in prod.
// eval.config.ts — version your eval battery in code
export const config = {
datasets: ["gold_v3", "adversarial_v2", "prod_sample_7d"],
evaluators: [
"retrieval_hit_rate",
"answer_faithfulness",
"pii_leakage",
"jailbreak_resistance",
"cost_per_request",
"p95_latency_ms",
],
gates: { faithfulness: ">=0.9", pii_leakage: "==0", p95_latency_ms: "<=1800" },
}How the engagement works.
Typically 2–6 weeks end to end, depending on surface area and access.
Discovery
We map your current LLM workflow, success criteria, and risk surface. Short, focused engagement to agree what "good" means for this system.
Dataset & Harness
We build a versioned eval dataset from real traffic, gold examples, and adversarial cases, plus the harness to run it.
Baseline & Report
We run the full battery against your current system and deliver a report with numbers, failure modes, and ranked recommendations.
Fix & Re-run
We implement the top fixes — retrieval, prompts, routing, guardrails — and re-run evals so every change is proven, not guessed.
CI Integration
We wire evals into your CI/CD so future changes are gated on quality, not just tests.
Ready to accelerate your tech growth?
Schedule your free consultation today and let's discuss how we can help your business scale efficiently.
