Bounded Autonomy is the production and operating system for AI agents in regulated operations. It discovers how your business actually works, learns from your best subject-matter experts, and compiles that expertise into governed digital workers — proven before they ship, and scored on every run in production.
One closed loop, four steps — discover the work, build the worker, prove it before it ships, then improve it on every run. Select any step to go deeper.
We learn how your business actually works — from the evidence, not the org chart.
An SOP is how leadership thinks the work is done; the evidence is how it's actually done. Discover closes that gap and hands Build a spec grounded in reality, not memory.
High-stakes, policy-bound, labor-intensive work — where every decision has to be defensible — is exactly where a governed worker earns its keep. The same factory pattern holds across regulated industries.
Benefits, eligibility, coverage, and claim-status inquiries — resolved end to end for members and providers, inside compliance guardrails.
Identity and sanctions checks, alert triage, and suspicious-activity narratives — assembled with a defensible audit trail.
First-notice intake, coverage determination, and policy-bound settlement — consistent, documented, and fully auditable.
Case intake and classification, evidence assembly, and regulatory-timeline tracking — with audit-ready resolution drafting that never misses a deadline.
Evidence assembly, policy-bound adjudication, and regulatory-timeline tracking — nothing slips a deadline.
Risk assessed against your guidelines, exceptions surfaced for review, and every decision backed by a documented rationale.
The edge isn't building one agent — anyone can. It's industrializing how every agent is built, governed, and run, so the difference compounds with each one.
Bounded Autonomy discovers how work actually happens, distills that knowledge into structured agent instructions and evals, then manufactures, evaluates, deploys, and operates AI workers inside the customer-controlled environment.
Conversations, SOPs, policies, traces, and evaluation artifacts remain inside your environment.
Operational knowledge is mined before agents are manufactured.
Agent behavior is derived from real workflows, policies, and SME validation.
Test cases and rubrics come from the same operational specification that guides the worker.
Production behavior is continuously evaluated against SOPs, policies, and expected outcomes.
Run traces, findings, and drift signals feed back into the next build cycle.
All operational data, knowledge distillation, agent build, evals, runtime, telemetry, and audit stay inside this boundary.
Bounded Autonomy discovers the operating model, converts it into structured instructions and evals, manufactures the worker, scores every production run, and feeds real-world learning back into the next version — all inside the customer-controlled environment.
We're choosing one founding customer in healthcare payer operations to harden the platform on a real problem — with a direct line to the founders and decisions in hours, not procurement cycles. If agents have to work, and have to be provable, let's scope it together.