Evaluation and Alignment for Production-grade AI

Move LLMs from "Experimental" to "Enterprise-Ready." We provide high-precision curation, red-teaming, and HITL evaluation to eliminate hallucination and bias.

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Introducing Precision

Harness expert human intelligence to audit, align, and deploy your AI with confidence.

Infrastructure

High-fidelity data pipelines for RAG and fine-tuning. We build the architecture that manages your curation lifecycle, ensuring your production data is secure, versioned, and audit-ready.

Infrastructure

High-fidelity data pipelines for RAG and fine-tuning. We build the architecture that manages your curation lifecycle, ensuring your production data is secure, versioned, and audit-ready.

Infrastructure

High-fidelity data pipelines for RAG and fine-tuning. We build the architecture that manages your curation lifecycle, ensuring your production data is secure, versioned, and audit-ready.

Workforce

Specialized RLHF and Red-Teaming led by domain experts. We move beyond generic labeling to provide the nuanced feedback required to eliminate hallucinations in high-stakes enterprise applications.

Workforce

Specialized RLHF and Red-Teaming led by domain experts. We move beyond generic labeling to provide the nuanced feedback required to eliminate hallucinations in high-stakes enterprise applications.

Workforce

Specialized RLHF and Red-Teaming led by domain experts. We move beyond generic labeling to provide the nuanced feedback required to eliminate hallucinations in high-stakes enterprise applications.

Benchmarks

Proprietary evaluation frameworks to measure model drift and bias. Our benchmarks provide a quantified "Truth Score" for your models, ensuring they remain safe and performant in the wild.

Benchmarks

Proprietary evaluation frameworks to measure model drift and bias. Our benchmarks provide a quantified "Truth Score" for your models, ensuring they remain safe and performant in the wild.

Benchmarks

Proprietary evaluation frameworks to measure model drift and bias. Our benchmarks provide a quantified "Truth Score" for your models, ensuring they remain safe and performant in the wild.