Production AI, engineered end to end, six eval-gated service lines.
The same playbook, tuned to the constraints of the sectors we ship into most.
Proof, not promises, selected case studies and recognition.
A transparent, 3-phase playbook from first audit to embedded team.
The senior team behind the work, and how to reach us.
A silent model regression reaches your customers, and your brand, before it reaches your dashboard. We build the eval suites, adversarial red-teaming, and drift monitoring that catch failures in staging, protecting both revenue and reputation.
Traditional QA catches code bugs. AI QA catches model regressions, safety failures, and retrieval drift. We cover both with the same rigour.
Custom evaluation harnesses measuring accuracy, hallucination rate, factual consistency, and toxicity on your specific domain and queries not generic benchmarks.
CI-integrated test suites that run on every model version change. A regression in eval score blocks the deploy, protecting users automatically.
Adversarial prompt testing to find injection vulnerabilities, jailbreak paths, and safety failures before they reach production or a press story.
Context relevance, faithfulness, and answer completeness testing across your retrieval pipeline. We find where your RAG system hallucinates and fix it.
Statistical monitoring of live model outputs against your eval baseline. Automated alerts when output quality drifts, before users notice.
Throughput and P95/P99 latency testing under realistic production load. We find the breaking point before your users do.
An eval suite written before the build defines what "working" means. Without it, you're shipping by vibes and calling it done when the demo looks good.
LLM providers update models frequently. What passed on GPT-4o last quarter may fail today. CI-integrated evals catch this automatically.
Safety is not a policy document. It's a set of scored test cases: adversarial prompts, PII leakage attempts, and refusal failure modes. We make it measurable.
Regulated industries and enterprise clients want proof, not promises. Eval reports give your stakeholders the evidence they need to approve AI in production.
An LLM eval is a scored test suite that measures how well your model performs on your specific use-case not a generic benchmark. Without evals, you have no way to know if a model update made things better or worse. We treat failing evals the same as failing unit tests: they block deploys.
Prompt injection vulnerabilities, system prompt leakage, jailbreak paths, bias in domain-specific responses, PII exfiltration risks, and model refusal failure modes. We document every finding and provide a remediation plan.
Yes. We run RAGAS and custom evals against your retrieval pipeline, identify the query types where faithfulness and relevance are lowest, and propose chunking, reranking, or retrieval strategy changes to fix them.
A basic CI-integrated eval pipeline for an existing LLM application typically takes 1–2 weeks. We connect it to your existing CI provider and set threshold alerts on day one.
30 minutes, one of our seniors, no slide deck. By the end of the call you'll know whether we're the right team, and if not, who is.