# Research — Ooak Data

Research

# We publish what we learn

Our research covers evaluation methodology, dataset design, and the gap between benchmark performance and real-world capability.

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## Why synthetic benchmarks are holding AI back

The AI industry over-indexes on synthetic benchmarks that don't reflect real-world complexity. Models that ace leaderboards routinely fail in production.

March 18, 2026

10 min read

](/research/why-synthetic-benchmarks-are-holding-ai-back)[

## The data problem in agentic AI

AI agents need fundamentally different data than chatbots: multi-step interaction trajectories, tool-use demonstrations, and multi-modal workflow data. This data barely exists.

February 11, 2026

11 min read

](/research/the-data-problem-in-agentic-ai)[

## From sandbox to production: the gap nobody talks about

80% of AI projects fail in production. The problem is not the models. It's the gap between evaluation conditions and deployment conditions.

January 22, 2026

12 min read

](/research/from-sandbox-to-production)