Building the Future of Automation with Oversight
We build agentic AI that can be trusted to act, not just to answer. The model does the work; independent checks watch what it sends out, what it commits, whether it stays on track, and what it can prove afterwards.
At the core is our patent pending Sentinel Project — a multi-provider agentic harness, built so an agent can be trusted with work that has consequences. Sensitive data never leaves in the clear. Nothing is committed unless it traces to a source. Our first wedge is commercial insurance, where agencies use Sentinel to cut back-office cost today — and to open new revenue opportunities next.
The Sentinel Project
The AI technology that builds and powers everything we make. General-purpose coding agents and copilots write software; Sentinel is a different category — an operational automation agent that does real work inside the live systems a business already runs on.
It runs long-horizon sessions across multiple AI providers — cloud and local — with enterprise-grade, persistent autonomous execution that is safe and secure by design. That heterogeneity is also what lets us lead on token-cost-driven economics.
And it is supervised while it does so — independent checks on what it sends, what it commits, and whether it is getting anywhere. That is the difference between a demo and something you can put in front of work that matters.
Sensitive Data Protection by Design
On-device models identify the sensitive values in your data — whatever they are for your domain — and substitute them before any request reaches a cloud provider, then restore them locally only to complete the action.
Provenance & Grounding
Every value the agent takes in or acts on carries its origin and confirmation status. Anything that traces to no source is flagged rather than committed.
Progress Supervision
An independent watcher follows where the work is actually heading, so a run that drifts off course is caught and corrected before it gets far.
Acts in Existing Systems
Operates the systems a business already runs on — web applications, legacy tools, and machines — without APIs or integration work.
Four things watch the agent while it works
General-purpose agents send everything they see to the model, enter whatever they conclude, and keep going when they're lost. None of that is acceptable once the work has consequences. Sentinel runs four independent checks alongside the model — each watching a different thing, none of them able to be talked out of it.
What it sends out
We treat the external model as untrusted with your data. Sensitive values are identified on-device and withheld — the model works on stand-ins and never receives the real thing, in a prompt, a transcript, or a log. What it does need to understand the work, it keeps.
What it commits
Every value the agent commits is checked against its source — the document it read, the screen it saw, what you told it. A value that traces to nothing is flagged, not committed. This is the check that stops something the model invented from becoming a fact in your business — the one that matters when being wrong is expensive.
Whether it's on track
Busy is not the same as right. A separate watcher follows where the work is actually heading, and when a run drifts the harness intervenes — graduated to how certain it is — before it gets far.
What it can prove
Every action, every value, every source, kept as a record you can go back to — and the record itself carries nothing sensitive in the clear. Months later you can show exactly what the agent did and what it was working from, to a customer, an auditor, or a regulator.
A foundation worth building on
AI rarely fails a business by being unable to do the work. It fails by doing it confidently and getting it wrong — and nothing you add on top can correct a foundation that invents things.
Sentinel is built to be that foundation. Whatever gets built on it inherits the same guarantees: sensitive data stays protected, nothing is committed that doesn't trace to a source, and work that drifts gets corrected rather than trusted. Those are the terms on which real responsibility can be handed to an AI system at all.
We're working with operators, founders, and developers who want to build exactly that. If there's work in your business that AI should be doing but can't yet be trusted with, we'd like to hear about it.
Let's Build Together
We're a small team working on big ideas. If you have automation challenges or want early access to what we're building, we'd love to hear from you.