The compliance check that catches what regex cannot
Catches the subtle compliance violations regex cannot — structure-function vs disease-claim drift, comparative claim ambiguity, state-specific dosage language — across every AI content output.
The problem
Your AI page generator writes supplement product descriptions. A regex pre-filter catches obvious violations like "cures cancer." But what about "supports immune function in clinically meaningful ways"? Regex cannot tell whether that crosses from an allowed structure-function claim into a regulated disease claim. Your outside counsel says it is borderline. You ship 100 product pages a week and counsel cannot review every one.
The categories of tools that touch this each handle a different problem. LLM evaluation platforms (Braintrust, LangSmith, Helicone, Patronus AI, Galileo, Arize, Fiddler, WhyLabs) check generic output quality — factuality, coherence, task completion. They do not know your regulatory rules. LLM guardrails (Guardrails AI, NVIDIA NeMo Guardrails, Lakera, Robust Intelligence) block unsafe or off-topic output — general AI safety, not per-vertical regulatory. AI governance suites (IBM watsonx.governance, Microsoft Responsible AI Dashboard, Credo AI, Holistic AI) produce enterprise audit reports, not per-content output scoring. Content moderation APIs (OpenAI Moderation, Google Perspective, Hive, Microsoft Communication Safety) filter user-generated content for hate speech and harassment, not brand-produced regulated marketing copy.
The gap is a semantic check that loads your specific regulatory rule libraries (FDA structure-function, FTC substantiation, FINRA suitability, OSHA, Prop 65) and scores every AI output for whether it might cross a regulatory line — so the borderline ones get reviewed and the clear ones flow through.
What success looks like
Your active rule libraries load into the semantic checker. Every AI content output gets scored for each rule — a confidence score from 0 to 1 of whether the output might violate the rule.
You set the thresholds. Clear pass scores auto-publish. High-confidence violations block automatically. Borderline scores (typically the 0.3 to 0.7 range) route to legal or compliance for review. The deterministic regex pre-filter handles the obvious cases first; this catches the semantic drift regex cannot see — structure-function language that veers into disease-claim territory, comparative claims that get ambiguous, state-specific dosage or efficacy language, FTC-required substantiation language without matching evidence.
Multi-vertical operators get per-vertical profiles. Multi-state operators (financial services) get per-state profiles. Every score is captured in the audit history for regulator inquiry response. The model calibrates against your team's overrides: as your reviewers approve and reject borderline cases, the score thresholds adjust to what your editorial team actually allows.
Braintrust and LangSmith stay useful for evaluating LLM quality. This handles the regulatory compliance side they do not.
How most operators solve this today
A few categories of tools touch this problem, but none of them load your specific regulatory rule libraries and score every AI output for compliance:
LLM evaluation platforms (Braintrust, LangSmith, Helicone, Patronus AI, Galileo, Arize, Fiddler, WhyLabs)
$0 to $200,000+/year
Evaluate generic LLM output quality (factuality, coherence, task completion). Per-vertical regulatory rule libraries require custom configuration.
LLM guardrails (Guardrails AI, NVIDIA NeMo Guardrails, Lakera, Robust Intelligence)
$0 to $3,000+/month, plus enterprise
Block unsafe or off-topic output. Built for general AI safety. Not per-vertical regulatory.
AI governance suites (IBM watsonx.governance, Microsoft Responsible AI Dashboard, Credo AI, Holistic AI)
$30,000 to $300,000+/year
Enterprise AI governance and audit reporting. Not per-content output scoring at publish time.
Content moderation APIs (OpenAI Moderation, Google Perspective, Hive, Microsoft Communication Safety)
Free to $50,000+/month
Filter user-generated content (hate speech, harassment, sexual content). Not regulated brand-produced content.
AI safety research tools (Anthropic Constitutional AI, Inspect / UK AISI, HELM / Stanford CRFM)
Research and academic
Research-focused. Not production-deployable per-vertical compliance.
Build it in-house
ML engineer ($150-220k) + compliance reviewer time + ongoing tuning
Per-vertical rule libraries built from scratch. Calibrating against your editorial team's overrides takes months.
What changes when this is an agent skill
Your active rule libraries — FDA structure-function for supplements and cosmetics, FTC claim substantiation restrictions, FINRA suitability, OSHA chemical, Prop 65, EU Cosmetic Regulation — load into a language-model-based checker. Every AI content output gets scored for each applicable rule with a confidence score from 0 to 1.
You set the thresholds. Clear pass scores auto-publish. High-confidence violations block automatically. Borderline scores route to legal or compliance for review. The deterministic regex pre-filter handles obvious cases first; this catches semantic drift regex cannot see.
Multi-vertical operators get per-vertical profiles. Multi-state operators get per-state profiles. Every score is captured in the audit history for regulator inquiry response. The model calibrates against your team's overrides — as your reviewers approve and reject borderline cases, the score thresholds adjust to match what your editorial team actually allows.
Braintrust and LangSmith stay useful for evaluating LLM quality. This handles the regulatory compliance side they do not.
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 loads your regulatory rule libraries and uses a language model to score every AI content output against them. Each output gets a confidence score per rule. Clear passes publish. Clear violations block. Borderline scores route to legal or compliance for review.
- How is this different from Braintrust or LangSmith?
- Those evaluate LLM output quality — factuality, coherence, task completion. This evaluates against your specific regulatory rule libraries (FDA structure-function, FTC substantiation, FINRA suitability).
- How is this different from Guardrails AI or NVIDIA NeMo Guardrails?
- Guardrails tools block unsafe or off-topic output — general AI safety. This scores AI content against per-vertical regulatory compliance specifically.
- How is this different from IBM watsonx.governance or Microsoft Responsible AI Dashboard?
- Enterprise AI governance suites produce audit reports and policy documentation. This scores each individual content output at publish time.
- How is this different from OpenAI Moderation API or Google Perspective?
- Content moderation APIs filter user-generated content for hate speech and harassment. This scores brand-produced AI content against your specific regulatory rules.
- What rule libraries does it score against?
- FDA structure-function (supplements, cosmetics), FTC claim substantiation restrictions, FINRA suitability, OSHA chemical labeling, FCC RF compliance, California Prop 65, EU Cosmetic Regulation, plus any custom libraries you add.
- How does this work alongside the regex pre-filter and review queue?
- The regex pre-filter catches obvious blocklist violations first, which is cheap. This catches the semantic violations regex cannot see — the cost only gets spent on outputs that need it. Borderline scores then route to the review queue.
- What confidence threshold should we set?
- Tunable per rule and per vertical. A typical default is below 0.3 auto-pass, above 0.7 auto-block, and 0.3 to 0.7 routes to review. The model recalibrates against your team's overrides over the first 60 to 90 days.