Attribution for the AI Content Era

Creators are being copied at scale.

ATTRI helps identify, verify, and track derivative content across platforms like YouTube using structured claims, similarity analysis, and human verification.

Signals Thumbnail, title, timing, and transcript comparisons to support review.
Verified Human-in-the-loop workflows turn reports into trusted attribution records.
Registry A growing database of relationships between original and derivative content.

The attribution gap in the AI content era

AI tools have made it easier than ever to remake videos, reuse winning hooks, recreate thumbnails, and republish the same ideas in slightly altered forms. Creators often discover these copies manually, with no structured way to document, verify, and track them over time.

Current reality

Discovery is manual

Most creators only notice derivative content after the fact, often by chance, after audience members or peers point it out.

Missing infrastructure

No shared system of record

There is no trusted, creator-first registry for documenting relationships between original and derivative content.

Why it matters

Repeat offenders stay invisible

Without structured claims and verification history, viewers and creators have no clear way to see patterns across channels.

The solution

ATTRI is building an attribution database for digital content

ATTRI is a system for verifying and tracking relationships between original and derivative content. Instead of trying to scan all of the internet upfront, ATTRI starts with creator-submitted claims and uses a human-in-the-loop workflow to build a trusted dataset over time.

Structured claims linking original and suspected content
Similarity analysis using key surface-level and content-based signals
Verification workflows that prioritize trust over guesswork
What gets stored

A growing dataset of attribution relationships

Every verified claim contributes to a structured database that can support creator protection, repeat offender tracking, browser extensions, and future attribution infrastructure across platforms.

Video metadata and publishing timelines
Similarity snapshots for title, thumbnail, timing, and transcript
Claim status, reviewer actions, and final classifications
Public attribution records for verified relationships

How ATTRI works

The first version focuses on speed, usability, and trusted verification. It is not trying to automatically detect every copy on the internet. It is building a reliable attribution layer one verified relationship at a time.

Step 1

Claim submission

A creator submits their original content alongside a suspected derivative video, plus notes about why they believe the relationship exists.

Step 2

Similarity analysis

ATTRI fetches metadata and compares signals such as thumbnail concept, title framing, publication timing, and transcript similarity.

Step 3

Verification and surfacing

Claims are reviewed, classified, and only verified relationships are surfaced publicly through browser tools and future integrations.

Join early access

We’re building the attribution layer for the AI content era. If you’re a creator, platform partner, investor, or early supporter, join the waitlist and follow the journey as ATTRI launches.