Specific Labs

Companies and AI model builders want agents that can handle real work, but the decisions, exceptions, handoffs, and judgment behind how work actually gets done are rarely captured in clean, usable data. Generic model intelligence is not enough to make agents reliable inside a specific business or codebase. Without access to the messy, private context of real production environments, AI systems are evaluated and trained on sanitized or synthetic problems that do not reflect what engineers and teams actually face.

Specific Labs licenses private, real-world enterprise codebases from companies and turns that data into benchmarks and training material for AI agents. Its flagship benchmark, Real-SWE, tests frontier AI models against tasks pulled directly from these production codebases, complete with the context and complexity of an existing product, and reports resolution rate (pass@1 averaged over five runs) and estimated cost across a leaderboard of current models. The company also runs data partnerships and referral programs to source this material, and publishes its own research, including an experiment giving Claude Code and Codex full control of a meeting notetaker business to observe how autonomous agents operate without human oversight.