ClaimGate

A publish gate for AI-assisted marketing content.

Point it at the copy you are about to publish and the evidence your organisation actually holds. It tells you which claims you cannot substantiate, which of your own policy rules you are breaking, and whether the asset is missing a disclosure the law now requires.

Exit code 1 means blocked. That makes it usable in CI, a pre-commit hook, or a publishing pipeline — which is where a gate stops mistakes instead of documenting them afterwards.

$ claimgate check campaign.md --evidence substantiation/ --policy policy.json

Claims requiring substantiation (14)
  ✗ [attribution] According to research at a leading university, 94% of finance teams waste more
      unsupported: the figure(s) 94% in this claim do not appear in any supplied evidence
  ✗ [statistic] thousands of businesses, and we are fully ISO 27001 certified.
      contradicted: evidence states "Acme Invoicing is not certified under ISO 27001"
  ~ [statistic] Meridian Freight cut their reconciliation time from 22 hours to 9 hours a week
      partially_supported: the case study confirms 22 hours and 9 hours, but not "always"
      try: Meridian Freight's finance team reduced reconciliation from 22 hours a week to 9

Policy findings (3)
  ✗ [prohibited] guaranteed
      absolute guarantees are unenforceable and are treated as misleading advertising
  ✗ [disclosure] AI disclosure missing
      required under EU AI Act Article 50 (in force 2 Aug 2026) and platform labelling rules

BLOCKED — 14 unsupported claim(s), 3 blocking policy finding(s).

Why this exists

Generative AI is now how a large share of marketing copy gets written, and the controls did not scale with it. The recurring finding across 2025–2026 is not that models are bad at writing. It is that a reviewer cannot tell a hallucinated statistic from a real one — there is no grammatical signal that separates them — so hallucinated content passes a casual read. Organisations that scaled AI content without a review gate did not reduce their content risk, they accelerated it.

The existing tools do not close this. Detectors tell you a text was AI-written, which is not the question. Generators produce more copy. Governance platforms tell you at the end of the quarter that you had incidents.

ClaimGate answers the question that actually decides whether an asset can ship: can this specific claim be traced to something we can point at?

Related MCP server: AgentGround Cross-Check Verifier

The three checks

1. Substantiation. Every checkable claim in the draft is extracted, then adjudicated against your evidence. The important part is how:

• A number in a claim must appear in the evidence. That is a string search, not a judgement call, and no model gets to overrule it. A fabricated statistic cannot be talked into a pass.

• Semantic support is decided by a model given only the claim and the evidence, required to name the evidence it relied on, and allowed to answer unsupported or unverified.

• If the model is unavailable, the key is missing, or the reply is malformed, the verdict is unverified — which blocks. It never fails towards "supported".

2. Policy. Your prohibited and restricted claims, written down once and enforced on every draft: guarantees, medical and financial claims, unsourced superlatives, named competitors, personal data.

3. Disclosure. From 2 August 2026 the EU AI Act (Article 50) requires disclosure of AI-generated content in specified circumstances, and Meta, TikTok and Google each require an AI label on realistic synthetic media in ads. A customer-facing AI-assisted asset with no disclosure is non-compliant however good the copy is — so it is checked as a property of the draft, not left to whoever uploads it.

Install

No dependencies. Python 3.9+.

pip install "claimgate @ git+https://github.com/caseone115/claimgate"

Not a bare pip install claimgate. That name on PyPI belongs to a different, unrelated project (an AI-agent test harness), so the bare name installs that tool, in which claimgate check does not exist. This product installs from its own source repository, which is also where its licence and its tests live. From a checkout, pip install -e . is equivalent.

Start here: the free starter kit

claimgate init my-project && cd my-project
claimgate check DRAFT-template.md --evidence evidence/ --policy policy.json --no-model

That writes a policy file you can edit, an evidence folder, a blank draft template, a six-question pre-publish checklist (including the AI-disclosure section) and a GitHub Actions job that gates every pull request. --no-model needs no API key and no network: it runs the deterministic checks, and it does catch inventing — a figure that appears in the draft and not in your evidence blocks, whatever wrote it.

The kit is free and stays free: https://github.com/caseone115/claimgate.

The full engine — model adjudication of every claim, JSON output for pipelines, the whole test suite — is a one-off download at https://teeterbot.gumroad.com/l/claimgate/LAUNCH39: US$39 while the launch is running, US$149 after it. It is the same code as this repository, MIT licensed; nothing is held back from the source.

Use

claimgate init my-project                  # the free starter kit
claimgate init-policy policy.json          # a starter policy to edit
claimgate claims draft.md                  # just list the claims
claimgate check draft.md --evidence ./evidence/
claimgate check draft.md --evidence evidence.json --policy policy.json --json
claimgate check draft.md --no-model        # facts and policy only, no API calls

Evidence is any directory of .txt/.md/.html files, or a JSON array:

[{"id": "spec", "title": "Product spec", "text": "..."},
 {"id": "case-1", "title": "Meridian case study", "url": "..."}]

Set DEEPSEEK_API_KEY for adjudication. Without it, --no-model still runs the deterministic checks — which is where the fabricated-figure guarantee lives.

What this is not

• Not a fact-checker. It does not search the web or decide what is true. It decides whether you can substantiate what you wrote, against evidence you supply. That is the question a compliance review actually asks, and it is the only one answerable without pretending to authority the tool does not have.

• Not a detector. It does not guess whether a text was AI-written.

• Not a lawyer. It enforces the policy you write. It flags where AI disclosure obligations appear to apply; it does not give legal advice.

Use it from an MCP client

The same checks run as an MCP server, so an agent can gate a draft before it publishes instead of a human remembering to. mcp/server.py needs no install — uv fetches its one dependency. Add this to your client's config:

{
  "mcpServers": {
    "claimgate": {
      "command": "uv",
      "args": ["run", "--directory", "<repo>/mcp", "<repo>/mcp/server.py"]
    }
  }
}

It exposes check_draft, check_draft_json, list_claims, check_policy_only and about. See mcp/README.md, including what it deliberately refuses to do.

The free starter kit is a second registry entry, io.github.caseone115/claimgate-kit. init_kit writes the policy file, the evidence folder, the checklist and the GitHub Actions job into a directory you name; check_kit then runs that gate offline with no API key. See mcp-kit/.

Tests

python tests/test_claimgate.py     # 55 checks — the engine
python tests/test_outreach.py      # 21 checks — the outreach guardrails
python tests/test_kit.py           # 44 checks — the starter kit
python scripts/test_mcp_server.py  # 19 checks — the MCP server, over real stdio

The suite is written against the product's promises, not its implementation: that no claim is invented, that a number absent from the evidence is never reported as supported, that anything uncheckable blocks, and that the same input always gives the same answer. tests/test_kit.py also runs the shipped README through the shipped policy, because a claim-checking product whose own front page carries a prohibited claim is the worst demonstration there is.

Licence

MIT.