Rooster Arsenal · Research Layer

1,000 sources doesn't make the research good.
Forcing them to check each other does.

A normal AI research workflow goes source → summary — and quietly ships contradictions as facts. The Claim Graph goes source → entity → overlap → contradiction → verification → graph. Every claim is a node. Confidence is earned by independent support. Conflicts get flagged — never averaged away.

SOURCE

Every document enters as a node — never as a summary.

→
ENTITY

Claims are extracted atomically: who, what, the exact value.

→
OVERLAP

Same-fact claims from different sources merge into one node.

→
CONTRADICTION

An independent auditor hunts conflicts — different numbers, hidden units, yes/no flips.

→
VERIFICATION

3+ sources agreeing = high confidence. 2 = medium. 1 = stays weak. Any conflict = flagged.

→
GRAPH

The output is a claim-by-claim evidence map you can defend — not a paragraph you hope is right.

Why this exists: when our content engines research a topic, dozens of sources come back. Any two of them can disagree — and a summary never tells you. The Claim Graph is the verification layer between "found sources" and "published as fact."

A real run, embedded

We pointed it at one of the internet's most-answered questions — "how much water should you drink a day?" — and 3 top-tier health publishers. A summary engine would have shipped one number. Here's what the graph caught:

3Sources checked
30Atomic claims extracted
25Claim nodes built
5Contradictions surfaced

⚡ How much water should you drink a day?

Harvard Health
four to six cups
VS
Healthline + WebMD (NAM/IOM)
15.5 cups men / 11.5 cups women

Two of the most-cited health publishers give answers 3x apart — and both get quoted as fact daily.

⚡ The IOM number itself

WebMD
13 cups men / 9 cups women
VS
Healthline + Harvard
15.5 / 11.5 cups

Even the sources citing the SAME institute report different figures — units quietly shifted (total fluids vs water).

A content pipeline without a claim graph publishes one of those numbers and never knows. Ours now asks which sources agree, which fight, and why — before a word goes live.

The confidence ladder

Every claim node gets a trust grade you can act on.

HIGH — 3+ sourcesIndependent agreement. Safe to publish.
MEDIUM — 2 sourcesSupported, but one more source would confirm.
WEAK — 1 sourceStays flagged. Never a headline number.
CONTRADICTIONSources fight. Surfaced with both sides + why — a human decides.

Your market, mapped claim-by-claim.

This is the layer under our research and content engines. Start with the free AI Visibility Scan — see what the machines already believe about you.

Get My Free AI Visibility Scan

Claim Graph — FAQ

What is a claim graph?

A research method where every source becomes atomic claims, same-fact claims merge into nodes, confidence is scored by independent support (3+ = high, 1 = weak), and an independent audit pass flags contradictions instead of averaging them away.

Why do normal AI research workflows ship wrong facts?

They go source → summary: conflicts get silently blended into confident prose. In one Claim Graph run, 3 top health publishers produced 5 contradictions on a single everyday question — a summary would have picked one number and shipped it.

Where does it run?

Inside our research and content engines — between "sources found" and "published as fact" — and in the reconnaissance we run for clients before any strategy is written.