Pre-approved claims library that every AI output checks before it ships
A living library of the claims your legal team has approved, with the evidence attached — every AI-generated post, page, ad, and product description checks it before it publishes.
The problem
Your VP of marketing says "clinically proven." Your founder says "most effective." A supplement page references a 2019 study legal has not vetted. A franchise earnings page quotes a figure that does not match the latest FDD. Each of those is a claim, and the FTC, FDA, and state attorneys general all enforce against the ones you cannot back up.
The trouble is, your AI agents do not know which claims are substantiated and which are not. Neither do most of your human writers — they pull phrases from last quarter's campaign without checking whether the underlying study, performance backtest, or methodology is still valid. The result is a steady leak of unapproved language across location pages, product descriptions, ads, and email.
The categories of tools that touch this problem each handle a slice. Legal research databases give your lawyers case-law access. Outside advertising counsel writes substantiation memos at hundreds of dollars an hour. In-house compliance reviewers bottleneck past 50 pieces of content a week. Channel-specific compliance tools enforce on one channel at a time. None of them maintain a structured library of the claims you have approved, with the evidence linked, that every AI agent in your stack consults at the moment it produces copy.
What success looks like
Every substantive claim your AI agents produce gets checked against an approved-claims library at the moment of output. Each approved claim is linked to its evidence — the clinical study, the comparative test, the performance backtest, the FDD Item 19 methodology — plus an expiration date and any state or vertical conditions that apply.
When an AI agent attempts a claim that is not in the library, it routes to legal for review. Approved claims get added with the evidence link. Rejected claims get added to your forbidden-phrase list with the rationale recorded.
When the underlying evidence ages out — a study is superseded, a performance period ends, a methodology goes stale — the claim auto-suspends across every AI agent until you re-substantiate it.
Multi-brand portfolios get separate libraries per brand. Multi-vertical operators get per-vertical extensions (FDA structure-function for supplements, FINRA suitability for financial restrictions). Franchise operators get FDD Item 19 earnings-claim discipline. Your outside counsel still approves new claims; the system makes their approvals operationally enforceable across every AI agent.
How most operators solve this today
A few categories of tools touch this problem, but none of them maintain the kind of approved-claims library your AI stack can actually check at output time.
Legal research databases (Westlaw, LexisNexis)
$5,000 to $15,000 per seat per year
Built for lawyers researching case law. Not built for operators, and no AI agent can consume the output.
Substantiation research firms (IMS Legal Strategies, Compass Lexecon, expert witnesses)
$5,000 to $20,000+ per substantiation memo
Each memo is authored by hand for a single high-risk claim. Memos sit in legal team drives. AI agents have no access to them.
Advertising compliance counsel (Kelley Drye, Manatt, Frankfurt Kurnit)
$400 to $800/hour, retainers of $50,000 to $200,000+/year
Approves claims and drafts substantiation memos. Approvals live in email threads. No structured library, no enforcement.
In-house compliance reviewer
$90,000 to $200,000/year salary
Single reviewer becomes the bottleneck past 50 pieces of content per week. Approvals live in Slack and Docs.
Single-channel compliance tools (PerformLine, Hearsay, Smarsh, ProofPoint)
$25 to $65 per user per month, or $30,000 to $150,000/year
Each one covers a single channel (affiliate, financial-services social, email DLP). They do not cover your full AI content stack.
Build it in-house
Senior engineer ($130-220k) + compliance reviewer time + ongoing maintenance
Six to twelve months for a v1. Per-vertical regulatory schemas and expiration handling are the hard parts; your team will rebuild what exists.
What changes when this is an agent skill
The system maintains an approved-claims library specific to your business. Each claim is linked to its evidence — the study citation, the performance backtest, the methodology — plus an expiration date and any state or vertical conditions. Every AI agent that produces content checks each substantive claim against the library before it publishes.
When an agent attempts a new claim that is not in the library, it gets routed to legal. Approved claims are added with the evidence linked. Rejected claims feed into your forbidden-phrase list with the rationale captured.
When evidence ages out — a clinical study is superseded, a performance period ends, a comparative methodology goes stale — the claim auto-suspends across every AI agent until it is re-substantiated.
Per-vertical filtering handles the regulatory variance: FDA structure-function for supplements and cosmetics, FINRA suitability for financial services, state-by-state restrictions for FTC Franchise Rule Item 19 for franchise earnings claims. Multi-brand operators get a corporate base library with per-brand overrides. Your outside counsel still approves new claims; the system makes their approvals operationally enforceable across every AI agent in your stack.
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.
Brand-Spec Authoring + Maintenance Agent
Produces and maintains the canonical brand spec every content-producing agent's brand-voice gate enforces.
FAQ
- What does the claims library actually do?
- It holds every advertising claim your legal team has approved, with the evidence linked (clinical study, performance backtest, methodology, FDD Item 19 numbers). Every AI agent that produces marketing copy checks each substantive claim against the library before publishing. New claims route to legal for review; expired claims auto-suspend.
- How is this different from Westlaw or LexisNexis?
- Those are for your lawyers to do ad-hoc case-law research. This is your operator-specific library of claims you have already approved, with the evidence attached, enforced at the moment your AI produces content.
- How is this different from outside advertising counsel?
- Outside counsel substantiates individual claims at hundreds of dollars an hour, then the memo sits in a folder somewhere. This makes those approvals operationally enforceable. Counsel still approves new claims; the library turns their decisions into something every AI agent in your stack actually consults.
- What about franchise earnings claims under FTC Franchise Rule Item 19?
- Item 19 earnings claims are supported with the substantiation methodology linked. Any franchisee-level deviation triggers a review before it ships.
- What about FDA health claims for supplements and cosmetics?
- The FDA distinguishes structure-function claims (allowed with substantiation plus disclaimer) from disease claims (which require pre-market FDA approval). The per-vertical filter enforces the distinction at the language level.
- How are expired or invalidated claims handled?
- Every approved claim has an evidence link and an expiration date. When the underlying study, performance period, or methodology ages out, the claim auto-suspends across every AI agent until it is re-substantiated.
- Can different brands or states have separate libraries?
- Yes. Multi-brand portfolios get per-brand libraries. Multi-vertical operators get per-vertical extensions. Multi-state operators get per-jurisdiction overrides. A corporate base library sets the defaults; brand, vertical, and state sub-libraries override or extend.