How We Improved Model Reliability Through Expert Evaluation
What changed when we graded model output against specialist judgment instead of benchmark scores, and how far the reliability gains carried into production.
Research
Data quality makes all the difference.
We’re driven by the conviction that model performance is fundamentally bounded by evaluation quality. Through expert collaboration, rigorous curation methodologies, and deep domain expertise, we research infrastructure that powers tomorrow’s reliable AI.
What experts know that benchmarks don't
Specialists disagree with benchmark verdicts in predictable places. We mapped where, and what that disagreement is worth as training signal.
Building the Infrastructure for Reliable AI
The engineering behind turning scattered domain review into evaluation and routing that a production system can actually depend on.
Solving the Deployment Problem
Where production models break down between benchmark performance and real professional use.
The Adzzat Thesis
Why model performance is bounded by evaluation quality, and what follows from taking that seriously.
How Expert Evaluation Drives Model Performance
The measurable link between domain-expert judgment and downstream model reliability.
Core research areas
Where we focus.
Intelligent Routing
Research into dynamic model selection, cost optimization, and quality-aware deployment.
Human Evaluation
Research into capturing domain expertise at scale and transforming judgment into training signal.
AI Safety & Reliability
Research into failure mode detection, robustness evaluation, and deployment safety.
Data Quality & Curation
Research into expert sourcing, quality verification, and dataset construction.