Policy lives in PDFs and wikis. Operations live in tickets, contracts, and systems of record. The gap between them is where control failures hide.
GRC and compliance teams sample a fraction of contracts, change tickets, or vendor packs. Full coverage is impossible when every check is a human reading two documents side by side.
Control owners screenshot evidence before audits, then re-do the same work next quarter. Nothing continuously compares "what the policy says" to "what the system shows."
When something fails, the trail is often a Slack thread. That is hard to defend and harder to trend.
We build agent workflows that extract obligations and control statements from policy and contracts, map them to operational signals (system state, tickets, configs, attestations), and flag deviations for a human owner.
The agent does not rewrite policy. It applies a written control library: if vendor SOC reports must be under 12 months old, that is a date check; if a change needs dual approval, that is a workflow check against the ticketing system.
Findings open cases with evidence attached. Owners remediate or accept risk with a recorded reason. Compliance can pull a report of open gaps without starting from zero at audit time.
Sense: connect to document stores, ticketing, IAM, cloud config, or vendor portals—whatever holds the operational truth for each control.
Reason: parse policy and control definitions into checkable assertions; run scheduled or event-driven evaluations; score confidence when interpretation is required.
Rock: open or close findings in your GRC or issue tracker; notify control owners; retain evaluation history for the audit window.
Treating policy language as free-form Q&A. Controls need IDs, owners, frequency, and pass/fail criteria—not a paragraph the model rephrases each run.
Checking only documents and ignoring system state. Many controls are about how production is configured today.
No owner loop. Flags without remediation workflows become noise and get ignored.
It is a system that continuously or on a schedule compares operational evidence to written policy and control requirements, then opens findings when they diverge. AI helps extract and map language; pass/fail is defined by rules compliance owns.
GRC platforms store controls, owners, and attestations. We build the automation that gathers evidence and evaluates state against those controls—especially when evidence lives outside the GRC tool. Often we integrate with an existing GRC rather than replacing it.
No. Compliance conclusions for material controls stay with control owners and compliance. The system proposes findings with evidence; humans accept, remediate, or risk-accept with a reason.
Start with a small control set that has clear evidence sources: vendor due diligence dates, access reviews, change approvals, backup configs. Expand after the first loop produces findings people trust.
Every check has an owner, severity, and de-duplication key. Repeated failures aggregate into one open finding. Low-confidence interpretations go to review, not a flood of emails.
Engineering services
Automated policy compliance checking is the kind of workflow we build as an AI agent system: deterministic checks, human checkpoints, and an audit trail operators can trust. We start from your real process boundaries and ship software that holds up in production.
We build production-ready, highly observable agentic systems engineered for enterprise scale. No black boxes, no magic—just systematized workflows with systemic safeguards.
We don't build fragile wrappers. Complex decisions and exceptions are automatically routed to your team for approval, ensuring zero unverified actions in production.
Every AI-generated output is validated against deterministic, programmatic rules before execution, guaranteeing structural integrity and compliance.
Our architecture records every state change, agent reasoning step, and user interaction, providing complete observability into your automated workflows.
Built for enterprise scale. We optimize for high-throughput, low-latency execution using edge infrastructure and efficient state management.
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