Validation
Every human makes mistakes. Every LLM hallucinates. The difference: We can measure how often — and systematically ensure it stays below a defined threshold.
A language model processing a tax return can fabricate amounts. A safety assistant can hallucinate regulatory references. A support agent can make commitments the company cannot keep.
Most AI providers ignore this problem — or rely on a “works most of the time” approach. We don't.
Derived from the methodology for autonomous vehicles (ADAS) and functional safety. Adapted for AI agents in enterprise environments.
Business case → max. error rate.
E.g.: <0.15% at 7,500 operations/year.
Prompt engineering + reference knowledge.
Domain expertise in files, not in LLM memory.
Hallucination detection.
Deterministic + semantic verification.
OODA Loop
TurboQuant
Qwen 27B
Iterative feedback loop:
Error → Prompt fix → Re-test
Every operation is automatically scored.
Quarterly error rate reporting.
2,000 synthetic scenarios.
Statistical proof with confidence interval.
Isolated tests per OODA phase.
Equivalence Classes + Boundary Values.
For every agent, we run multi-day validation cycles with thousands of test cases on our GPU infrastructure. Each individual test case is evaluated with AI support — deterministically for numbers and facts, semantically for tone and context. Only when the measured error rate falls below the agreed acceptance criteria does the agent go into production. We invest this effort for every single agent.
Python code, no LLM. 100% reliable, <1 second.
Separate LLM call, low temperature. Calibrated against gold standard.
Calibration: Each check prompt is calibrated against hand-curated gold drafts (known good + known bad). Precision, recall, and F1 score are measured. Only checks with F1 > 0.9 are deployed. The details of our calibration methodology are proprietary.
KV cache compression to 3-bit (ICLR 2026). 6× more context on the same GPU. Zero accuracy loss.
Small draft model generates, large model validates. 2.5× faster inference at identical quality.
Every statement by the agent is broken down into atomic claims and verified against source data. Based on FActScore and Chain-of-Verification (Meta 2023).
Our methodology is based on standards developed for autonomous driving and ADAS systems. We are the first to systematically apply this methodology to AI agents in enterprise environments.
Acceptance criteria, Monte Carlo, scenario generation, V&V process
AI Safety Requirements, Input Space Refinement, Output Insufficiencies
Validation of Intended Functionality, Residual Risk, Trigger Conditions
HAZOP, FMEA, deterministic Safety Measures, process maturity
The complete validation methodology is part of our consulting services. We'd be happy to show you in a personal meeting how we set up your AI agent so you can rely on it with confidence.
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