WildCaught runs quietly on your team's screens, learns the repetitive work, and automatically builds the agents and models to take it off their plate. No setup. No prompting. No cost.
WildCaught learns the job by watching it get done, then quietly automates the parts that don't need a human.
Captures screen, application context, and the work artifacts your team produces, as it happens. No keystroke logging.
Spots the repetitive, rule-bound tasks and surfaces exactly where an agent or model would actually save time.
Automatically generates the agents and models to handle that work. No engineering project, no prompt writing.
You approve what runs. The automation takes over the busywork, and your people get their time back.
Every repetitive, rule-bound task your team does is a candidate. WildCaught finds the ones worth automating and builds the agents to clear them, so your skilled people spend their time on the work that actually needs them.
And it compounds: the longer it runs, the more it can take off their plate. No engineering project, no new headcount, nothing to configure. It's free to put in your team's hands from day one.
Real expert work, caught in the wild the moment it happens, is the one thing frontier models can't generate for themselves. It's the opposite of synthetic.
Frontier AI labs pay to learn from that work. That's what makes WildCaught free for your team. Capture is consented, aggregated, and rights-clean. Your team gets a tool that automates their day, and the value of the work funds it.
WildCaught is built to be scoped down and switched off. Your team stays in control of every frame.
Choose which apps, sites, and workflows are ever in scope. Exclude or redact anything sensitive so it's never recorded in the first place.
Set the rules for what can leave your team. Hold back entire categories of data, and the rest never has to move.
Pause or stop capture anytime, for one person, one app, or a single moment you'd rather keep private.
Nothing is captured without consent, and provenance travels with the data. Privacy isn't a cleanup step bolted on later.
Your data stays yours. WildCaught is the custodian, not the owner, with a DPA, clear retention rules, and deletion on request.
What reaches labs is aggregated and stripped of identifiers. Never your raw secrets, credentials, or who did what.
WildCaught is built enterprise-first, with the controls your security team expects on by default.
Your team's data is encrypted in transit and at rest, with strict access controls around every frame we capture.
SSO and SAML, SCIM provisioning, and role-based access, so the right people see only what they should.
One console to set policy, scope what's captured, roll out across the fleet via MDM, and audit every action.
Data-residency options, a DPA, and policy controls, so procurement, legal, and security can sign off.
The expert, agentic training data frontier models can't generate, captured across thousands of real jobs the moment the work happens. Available to a small number of labs under structured scarcity.
Genuine expert work product, not model-generated approximations that collapse over time.
Full trajectories of screen, context, and artifacts, captured as work happens, not reconstructed after the fact.
Skilled operators doing real jobs across thousands of workflows. The depth labs are paying billions to acquire.
Consent and provenance are part of the capture, not a cleanup problem bolted on later.
Teams: deploy WildCaught free and start automating the busywork. Labs: talk to us about wild-caught training data access.
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