Turn regulator PDFs into AI-enforceable compliance rules
AI-based parsing of FDA guidance, FTC actions rules, FINRA notices, EU directives, and your own policy docs into proposed rule updates your team reviews — with the source citation attached.
The problem
The FDA publishes a 47-page guidance document on cosmetic structure-function claims. Your outside counsel charges $400 an hour to read it and draft compliance rules. Six weeks later, you have a 12-rule update your compliance team adapts to your SKUs. Meanwhile the FTC issues a new enforcement action against a competitor — different source, different format, another consultant engagement. Each new update is the same story.
The categories of tools that touch this each handle a different problem. Compliance automation platforms (Drata, Vanta, Secureframe, Tugboat Logic, Hyperproof) automate SOC 2, ISO 27001, and HIPAA security compliance evidence collection — not marketing-content rule extraction. Policy management software (PowerDMS, ConvergePoint, Compliance Manager GRC, MetricStream Policy, NAVEX Policy Manager) manages policy documents and attestations but does not extract enforceable rules. Legal AI contract analysis tools (Harvey, Hebbia, Kira Systems, Luminance, LawGeex, Spellbook) review contracts for legal counsel, not regulatory rules for marketing. Document intelligence platforms (Microsoft Document Intelligence, Google Document AI, Amazon Textract, ABBYY FineReader) provide generic OCR and entity extraction; you build the rule extraction pipeline on top. In-house compliance plus outside counsel reads documents manually — a 6-month engagement to build one vertical rule library.
The gap is AI-based parsing that turns regulatory source documents into proposed compliance rules your AI agents can actually enforce — with the source citation attached so you can defend the rule to a regulator.
What success looks like
AI-based parsing ingests FDA guidance PDFs, FTC enforcement actions, state attorney general rules, FINRA notices, EU directives, OSHA standards, FCC orders, state regulator bulletins, your own internal policy documents (brand guidelines, legal SOPs), and industry association guidance (CTIA, NAD, BBB).
The extracted rules become proposed updates to your per-vertical and per-state rule libraries. Every extracted rule cites its source document plus the page and section it came from, so you can show a regulator exactly where the rule comes from. Proposed updates flow through your normal multi-stakeholder review before they go live.
When a source document is updated, the system re-extracts the rules and surfaces the diff. Conflicts across sources — FDA OPDP guidance versus an FTC precedent versus a state enforcement action — get surfaced for your team to resolve.
Every extraction, activation, and customization is captured in the audit history for regulator inquiry response. Harvey, Hebbia, and Kira stay useful for contract review. Drata and Vanta stay useful for security compliance audit. This handles the marketing-content rule extraction layer they do not.
How most operators solve this today
A few categories of tools touch this problem, but none of them parse regulator source documents into enforceable marketing-content rules:
Compliance automation platforms (Drata, Vanta, Secureframe, Tugboat Logic, Hyperproof)
$7,500 to $200,000+/year
Built for SOC 2, ISO 27001, and HIPAA security compliance evidence collection. Not marketing-content rule extraction.
Policy management software (PowerDMS, ConvergePoint, Compliance Manager GRC, MetricStream Policy, NAVEX Policy Manager)
$5,000 to $50,000+/year
Manages policy documents and attestations. Does not extract enforceable rules.
Legal AI contract analysis tools (Harvey, Hebbia, Kira Systems / Litera, Luminance, LawGeex, Spellbook)
$25,000 to $300,000+/year
Built for contract review and clause extraction. Not regulatory rule extraction for marketing.
Document intelligence platforms (Microsoft Document Intelligence, Google Document AI, Amazon Textract, ABBYY FineReader)
$0.10 to $50 per 1,000 pages
Generic OCR and entity extraction. You build the rule extraction pipeline on top.
In-house compliance team plus outside counsel
$150,000 to $300,000/year, plus $400 to $800/hour outside counsel
Manual rule extraction from FDA, FTC, and state attorney general source documents. A 6-month engagement to build one vertical rule library.
Build it in-house
ML engineer + compliance reviewer + outside counsel time
Parsing regulator PDFs into enforceable structured rules with source citation is a research problem. Falls apart past 1 regulated vertical or 3 actively-monitored regulators.
What changes when this is an agent skill
AI-based parsing ingests FDA guidance PDFs, FTC enforcement actions, state attorney general rules, FINRA notices, EU directives, OSHA standards, FCC orders, state regulator bulletins, your own policy documents (brand guidelines, legal SOPs), and industry association guidance (CTIA, NAD, BBB).
Extracted rules become proposed updates to your per-vertical and per-state rule libraries. Every extracted rule cites its source document plus page and section. Proposed updates flow through your normal multi-stakeholder review before they go live.
When a source document updates, the system re-extracts the rules and surfaces the diff. Conflicts across sources — FDA OPDP guidance versus FTC precedent versus state enforcement — get surfaced for resolution.
Every extraction, activation, and customization is captured in the audit history for regulator inquiry response. Harvey, Hebbia, and Kira stay useful for contract review; Drata and Vanta for security compliance audit. This handles the marketing-content rule extraction layer.
Agents that include this skill
Skills live inside agent rentals. To get this skill in production, hire any of the agents below — context-tuning at onboarding is included in the first month.
Vertical Compliance Overlay Manager Agent
Produces and maintains per-vertical + per-jurisdiction compliance overlays every content-producing agent loads at runtime.
FAQ
- What does this actually do?
- It uses AI to parse regulatory source documents — FDA guidance PDFs, FTC actions, state attorney general rules, FINRA notices, EU directives, your own policy docs — into proposed compliance rules your AI agents can enforce. Every extracted rule cites the source document, page, and section.
- How is this different from Drata, Vanta, or Secureframe?
- Those automate SOC 2, ISO 27001, and HIPAA security compliance evidence collection — built for IT and security audit. This extracts marketing-content rules from regulatory source documents.
- How is this different from Harvey, Hebbia, or Kira Systems?
- Legal AI contract analysis tools extract clauses from contracts for legal counsel review. This extracts structured rules from regulatory source documents (not contracts) for AI marketing content enforcement.
- How is this different from Microsoft Document Intelligence or Google Document AI?
- Those provide generic OCR and entity extraction. You build the rule extraction pipeline on top. This is the purpose-built regulatory rule extraction layer.
- What source documents does it parse?
- FDA guidance PDFs, FTC enforcement actions, state attorney general rules, FINRA notices, EU directives, OSHA standards, FCC orders, state regulator bulletins, your own internal policy documents (brand guidelines, legal SOPs), industry association guidance (CTIA, NAD, BBB).
- How does this work alongside the per-vertical and per-state libraries?
- Extracted rules become proposed updates to those libraries. Your team reviews the proposed updates through your normal multi-stakeholder review before they go live.
- How does this work alongside the regulatory monitoring layer?
- The monitoring layer surfaces source document updates. This re-extracts the rules from the updated documents, and the library diffs surface for review.
- What about defending a rule to a regulator?
- Every extracted rule cites its source document plus page and section, so you can show a regulator exactly where the rule came from. The audit history captures every extraction and every customization.