Private SWE-bench for your repo.

Private EvalOps is a private SWE-bench for your own repository. Tensor Cortex builds evaluation tasks from real work in your codebase, runs coding agents against them in a sandbox, and grades pass/fail with hidden tests and verifiers so you get evidence instead of vibes about which agent setup actually works on your code.

Public benchmarks measure performance on open repositories that models may have seen during training. Private EvalOps measures performance on your codebase, with your conventions, your test suite, and tasks that reflect the work your team actually does — the question that decides procurement.

The same private task suite can be run across Codex, Claude Code, Cursor-style agents, open-weight models, and internal agent loops. Because every agent runs the identical tasks under the same sandbox and verifiers, the comparison is fair and repeatable.

Hidden tests and verifiers decide pass or fail — the agent's own self-report is never the score. Runs are sandboxed and content-addressed, and verification can run network-off, so results are reproducible rather than one-off demos.

A first pilot is a fixed-scope evaluation engagement, not a subscription — typically 10–20 private repo tasks across 2–3 agents for $3k–$5k. A deeper custom suite runs $7.5k–$15k, and an ongoing monthly regression gate is $2k–$10k per month.

Yes. Your source code and hidden tests stay on your machine — runs are local-first and nothing is uploaded by default. You receive the evidence package: per-agent pass/fail, diffs, verifier output, and a failure taxonomy.

A repeatable task suite plus an evidence package: pass/fail scores per agent, configuration, git SHA, eval hashes, and failure notes — enough to make a confident decision and to catch regressions whenever prompts, tools, models, or guardrails change.