Tool use
Agents act, not just answer. Scoped, permissioned tools let them research, draft, and execute across your systems.
Everything an AI agent needs to do real work reliably: tools, retrieval, memory, and the guardrails that keep it correct.
The components we build, operate, and improve so our ventures don't have to.
Agents act, not just answer. Scoped, permissioned tools let them research, draft, and execute across your systems.
Hybrid retrieval over your documents and data, so answers are grounded in your knowledge, not the model's guesses.
Long-running agents stay coherent. We compress history without losing the facts that matter, run after run.
Durable memory across sessions and workflows, so agents learn your context and stop asking twice.
Pods of specialized agents, each owning a stage of a workflow, coordinated toward a finished deliverable.
Every capability is measured. Regression suites and live monitoring catch drift before your users do.
In regulated industries, data handling is the first objection to AI, and it should be. We prioritize open-weight models you can run where your data lives, scope every tool's permissions, and keep a human in the loop by default.
A venture goes from expertise to always-on agents in four steps.
We load the harness with a field's documents, processes, products, and rules of engagement.
We assemble a pod of specialized agents that each own a stage of the workflow.
We measure quality against real cases and tune until it clears the bar for the field.
The pod runs continuously, monitored for drift, with humans reviewing what matters.
If you know an industry inside out, we can equip agents with that expertise. Let's talk.
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