DATA FEEDBACK & EVALUATIONS

Driving AI quality and performance

We pair human expertise with advanced frameworks to rigorously test AI systems. Our human-in-the-loop approach drives continuous model refinement, risk mitigation and trustworthy outcomes at scale.

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Jaime, Sales, USA

From data integrity
to model excellence

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Data curation & annotation

High-quality, domain-specific data sourced, structured and labeled to build high-quality training sets with strict QA, ensuring models learn from accurate, representative inputs

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Model evaluation & red‑teaming

Comprehensive performance, safety and bias testing, including adversarial simulations to surface vulnerabilities, validate accuracy and meet regulatory standards before deployment

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Human-in-the-loop feedback

Real-time human review integrated into model lifecycles — ranking outputs, refining prompts and feeding insights back for continuous accuracy and usability gains

Accelerating AI transformation

With specialists in frontier model development, agentic AI design and CX automation, our teams share insights around research, safety engineering and operational precision, so every client benefits from the full breadth of our experience.

Case study

Standardizing RLHF for better AI outcomes

We helped a European AI firm fix inconsistent Reinforcement Learning from Human Feedback (RLHF) reviews by building diverse teams, standardizing guidelines and adding audit loops — improving accuracy, fairness and alignment with human values.

Read the case study