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Evaluation, Benchmarking & Red Teaming

Comprehensive AI system evaluation, performance benchmarking, and adversarial red teaming to identify vulnerabilities, weaknesses, and ensure robustness.

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Healthcare & Life Sciences

Healthcare AI Integrity & Clinical Governance

Texas forced an AI firm to admit its '0.001% hallucination rate' was a marketing fantasy. Four hospitals had deployed it. 🏥

0.001%

hallucination rate claimed by Pieces Technologies -- deemed 'likely inaccurate'

Texas AG Settlement (Sept 2024)

of companies achieving measurable AI business value at scale

Enterprise AI ROI Analysis (2025)

Interactive Technical

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Beyond the 0.001% Fallacy

Healthcare AI vendors market statistically implausible accuracy claims while deploying unvalidated LLM wrappers in life-critical clinical environments.

FABRICATED PRECISION

Pieces Technologies deployed clinical AI in four Texas hospitals claiming sub-0.001% hallucination rates. The Texas AG found these metrics 'likely inaccurate' and forced a five-year transparency mandate. Wrapper-based AI strategies built on generic LLM APIs cannot deliver verifiable accuracy for clinical safety.

VALIDATED CLINICAL AI

Implement Med-HALT and FAIR-AI frameworks to benchmark hallucination against clinical ground truth
Deploy adversarial detection modules 7.5x more effective than random sampling for clinical errors
Enforce mandatory 'AI Labels' disclosing training data, model version, and known failure modes
Architect multi-tiered safety levels with escalating human-in-the-loop for high-risk decisions

Retrieval-Augmented GenerationAdversarial DetectionMed-HALT EvaluationClinical Knowledge GraphsHuman-in-the-Loop

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Veriprajna Deep Tech Consultancy specializes in building safety-critical AI systems for healthcare, finance, and regulatory domains. Our architectures are validated against established protocols with comprehensive compliance documentation.