Agents do the reading. People make the decisions.
AI agents for compliance that build your company profile, read your sources, triage each finding and answer questions over your records. Every alert cites its source, and your team decides what happens next.
Compliance can’t rest on answers nobody can check.
General-purpose chat answers fluently and keeps no record. A compliance team has to know which sources were read, what was found, what was left out and why, and who made the call.
- An answer without the source paragraph can’t go in an audit file.
- Silent filtering means no one knows what was missed.
- Automation that acts alone moves accountability to a model.
The profile agent learns your business
From your website and a short brief, it drafts your company profile, then suggests jurisdictions, topics and sources. Your team edits and confirms each step.
Monitor and triage agents read and explain
The monitor agent reads your sources and cites what it finds. The triage agent scores relevance and urgency against your profile and writes the alert, with its reasoning attached.
The assistant answers with citations
Ask the Compliance Assistant about your alerts, obligations and policy text. Each answer cites the records it used.
What you get
Read in the original language
Agents read each source in the language it publishes in and keep the excerpt verbatim. The reasoning is written in English.
What was set aside, and why
Duplicates, exclusions and findings that don’t apply are kept with the reason, so a reviewer can check what the agents didn’t raise.
Provenance on every finding
The source URL, the publication dates and the verbatim excerpt stay with the alert, and the obligation converted from it links back to them.
Reasoning you can read
Each alert shows the triage agent’s relevance score, urgency, recommendation and reasoning beside the source it rests on.
People own the outcome
Converting an alert into an obligation, assigning an owner and dismissing an alert with a reason are actions people take, and the record shows who took them.
Runs on your schedule
Agents run on each watchlist’s schedule or on demand. A source that stops answering shows it in its health instead of going quiet.
Compliance Assistant
Grounded in your alerts, obligations and policy text. Add an attachment or search the web, and every answer cites its sources.
Your data and the models
Customer content is not used to train generalized AI models by default. In Private VPC, the agents run in your own cloud account and call the model providers with API keys you supply.
Questions
How is this different from ChatGPT or a news feed?
ChatGPT answers a question when you ask one, and a news feed lists what was published. RegWatch is a system of record for regulatory change. It reads the sources you choose on a schedule and keeps every finding with its source, its dates and the original text. It explains which changes apply to your business and why, and records what your team did about each: the owner, the obligation, the evidence and every decision. When an auditor asks what you knew and when, the answer is already on file.
Can the agents act without a person?
They read, score and write alerts on their own, on the schedule you set. Owners, obligations and dismissals are decided by your team, and a dismissal needs a written reason.
Is our data used to train the models?
Customer content is not used to train generalized AI models by default. To triage and answer, RegWatch sends the relevant text to its model providers; in Private VPC, those calls leave from your own cloud account.
What happens when a source fails?
The source’s health changes to warning or failing, so your team sees the gap instead of a silent miss.
Guides
- AI Agents for Regulatory Compliance: What They Actually DoVendor pages describe compliance agents in concepts and rarely show an execution. A complete run record shows the steps, timestamps, tokens and cost, and logs failure as a status an auditor can check a year later. A capability and limitation matrix lets you score any vendor.
- LLM Accuracy on Regulatory Text: Hallucination Risks and How to Control ThemChatbots got 58% to 88% of verifiable case-law questions wrong in published studies, and retrieval-based legal research tools 17% to 33% of queries. A six-domain checklist pairs each of its 25 controls with a pass or fail test and a framework mapping.
- Can You Use ChatGPT for Regulatory Horizon Scanning? What Breaks and WhyChatGPT is good at explaining a regulation you already know about and unreliable at telling you what your regulators published last month. This essay documents five failure modes with primary sources, an access audit of four regulator websites, and a five-prompt stress test you can run yourself.
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