TIER 3 — SPECIALIZED
QA & Testing for AI Systems
We've been doing QA for 25 years. Now we're applying that discipline to the hardest testing challenge in modern software: non-deterministic AI systems.
THE CHALLENGE
Traditional QA assumes deterministic outputs. AI doesn't work that way.
Our team implemented an AI test generation tool for a major open-source foundation — real work at the intersection of decades of QA expertise and modern AI systems. The same combination of traditional QA discipline and AI-native understanding that Proticom applies to client engagements.
AI TESTING SCOPE
What we build
Structured frameworks for evaluating LLM outputs against ground truth, business requirements, and quality thresholds. Systematic, repeatable, measurable.
When you update a model, prompt, or RAG configuration, what changed? We build the regression infrastructure that answers that question reliably.
Deliberate adversarial inputs — prompt injection attempts, edge cases, out-of-distribution queries — to find failure modes before production finds them for you.
Inference latency, throughput, and cost benchmarking across model options. Data-driven model selection, not vendor preference.
Production monitoring of AI output quality over time — detecting degradation, distribution shift, and hallucination rate changes as they emerge.
DEFINE YOUR TEST STRATEGY