Service
AI Quality Engineering
Independent, vendor-neutral evaluation and quality assurance for enterprise AI — so it ships accurate, safe, compliant and cost-effective.
What we do
We help you choose, evaluate, quality-assure and govern the right AI for your use case — across OpenAI, Anthropic Claude, open-source models (Llama, Mistral) and emerging ones. We don't sell a model; we bring the decision and quality competence around it, the same way we've always been tool-agnostic in testing.
Our approach
The same shift-left discipline we bring to software testing, applied to AI. Evaluation and QA run through the whole chain: use case → model selection → architecture (RAG, agents, guardrails) → objective evaluation → security & governance → production monitoring. We measure on your real workload, not vendor benchmarks.
The model bake-off
We run the same agent, RAG assistant or test-data generator on each model and score it objectively — quality, hallucination rate, tool-use reliability, latency, cost, data residency and prompt-injection resistance. That is what makes 'vendor-neutral' a fact rather than a claim.
Why Thinqist
We are a quality-assurance house first: independent, hands-on and at home in regulated DACH environments. We evaluate models together with their deployment options (EU region, on-prem, sovereign), so data governance is part of the decision — and we make AI systems audit-ready for the EU AI Act.
What we measure
We run the same task on each candidate model and score these dimensions on your workload — not vendor benchmarks. Your use case decides which model wins.
What you get
- A model bake-off scored on your own workload
- Agent, RAG and prompt / guardrail test suites
- AI security & red-teaming report
- Synthetic, DSGVO-compliant AI test data
- EU AI Act risk classification & documentation
- Continuous evaluation in production
Models & stack we work with
Related: Test automation
Related: Security testing
Want to know which AI is right for your use case — and prove it's safe?
Talk to us