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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.
25 years of QA discipline, applied to systems that can't be tested deterministically.
PROOF POINT
Real work at the intersection of QA and AI
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
01
01
EVALUATION HARNESSES
Structured frameworks for evaluating LLM outputs against ground truth, business requirements, and quality thresholds. Systematic, repeatable, measurable.
02
02
REGRESSION TESTING FOR AI
When you update a model, prompt, or RAG configuration, what changed? We build the regression infrastructure that answers that question reliably.
03
03
RED-TEAMING & ADVERSARIAL TESTING
Deliberate adversarial inputs — prompt injection attempts, edge cases, out-of-distribution queries — to find failure modes before production finds them for you.
04
04
PERFORMANCE BENCHMARKING
Inference latency, throughput, and cost benchmarking across model options. Data-driven model selection, not vendor preference.
05
05
OUTPUT QUALITY MONITORING
Production monitoring of AI output quality over time — detecting degradation, distribution shift, and hallucination rate changes as they emerge.
DEFINE YOUR TEST STRATEGY
Book a technical conversation — testing strategy starts with understanding what failure costs.
contact@proticom.com
844.PROTICOM
proticom.ai
844.PROTICOM
proticom.ai