Day one · Feature 01 of 10+ is liveFrontier evals, red-teaming, post-training data and RL environments — for the teams building the most capable AI.
Three years of working inside frontier models, every day, are now turning into tools. Probe is the first one out of the lab. The rest are being built in the open — some of what's next doesn't have a name yet.
Paste your AI's system prompt and the one thing it must never do. Probe scores its defences, builds a tailored attack suite across 11 techniques, and fires it live — scored by a hidden canary, not an opinion.
Run a free scan →Daily behaviour readings for every frontier model — sycophancy, refusals, injection, drift.
Your AI, tuned to how you work in seven days — with before-and-after proof.
Attacks that unfold across a whole conversation, the way real ones do.
An open arena where anyone can try to break an agent — every win becomes a test.
Preview AI-video plans, repair prompts and check estimated credits before rendering. Zero-credit animatic.
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The ideas we haven't had yet. Follow along — we ship in public.
Top tier of ChatGPT users — by ChatGPT's own ranking
Daily, adversarial use across frontier models
Ways we make models better: evals, red-teaming, data, environments
Benchmarks tell you how a model scores. We tell you how it behaves — where it breaks, why, and what data fixes it.
Before we change anything, we measure it. Attention, outputs, failure clusters, judge agreement — so every fix is aimed, not guessed.
Custom benchmarks built from your real use cases — not leaderboard trivia. Capability elicitation, LLM-as-judge calibrated against expert humans, contamination checks, and a harness you keep.
Adversarial testing by people who live inside these models. Jailbreak discovery, prompt-injection audits for agents, refusal calibration and reward-hacking hunts — every finding reproducible.
Expert-written SFT demonstrations, preference pairs for RLHF and DPO, and reasoning traces — designed against the failures we found, and measured on the evals that matter.
Verifiable tasks with graders that can't be gamed — tool-use, browser and workflow sandboxes where agents roll out, fail, and get better. Built for long-horizon reliability.
The failure modes we hunt, shown as illustrative examples — the kind of thing a 72-hour teardown surfaces, each with its fix.
We attack your model or agent for 72 hours straight. You get a ranked failure report, reproducible prompts and a fix for each.
A custom eval suite for your product, a harness you keep, and baselines across the frontier models you're choosing between.
Your embedded red-team and data partner, release after release — preference data, reasoning traces and RL environments on demand.