Documentation Index
Fetch the complete documentation index at: https://docs.factagora.com/llms.txt
Use this file to discover all available pages before exploring further.
What is Fingerprint?
Fingerprint is Factagora’s content provenance API. It solves a fundamental problem: how do you prove that a piece of content originated from you, even after it’s been rewritten, paraphrased, or translated? Traditional text-matching tools fail when content is rephrased. Fingerprint works differently — it analyzes the causal structure of your content (who did what, when, and why) and embeds an invisible watermark based on that structure. Even if every word is changed, the underlying structure remains detectable.How it works
Embed
You send your content to
/fingerprint/embed. The API extracts a Temporal Knowledge Graph (TKG) — entities, timelines, causal relations, and argument chains — then embeds an invisible zero-width Unicode watermark seeded by that structure. You get back the watermarked content to distribute.Detect
When you encounter suspicious content, send it to
/fingerprint/detect. The API runs two independent checks:- Watermark check — extracts hidden bits and correlates against stored fingerprints (near-certain match)
- TKG matching — compares the causal structure using fuzzy word-level matching (catches paraphrases)
Key capabilities
Paraphrase-resistant
Word-level fuzzy matching means “EU enacts AI Act” matches “European Union passed AI Act regulation” — no exact string match required.
Dual-layer detection
Watermark correlation (cryptographic) + TKG Jaccard (semantic) run independently. Both firing simultaneously makes coincidental overlap statistically implausible.
Content-type aware
Scoring weights are automatically tuned: legal documents emphasize causal structure (0.6), news emphasizes entities (0.5), reports balance all three signals.
Auditable evidence
Every match includes the exact overlap lists — shared entities, timelines, and causal triples — so you can explain why two articles matched.
What’s in a TKG snapshot?
The Temporal Knowledge Graph extracted from your content contains four layers:| Layer | Example | Purpose |
|---|---|---|
| Entities | Bank of Korea, Interest rate | Who and what are involved |
| Timeseries | 2024-03-15 | When events occurred |
| Relations | Bank of Korea → raises → Interest rate | What happened (cause and effect) |
| Argument map | Premise → Evidence → Conclusion chains | Why it happened (macro-level reasoning) |
Use cases
- News agencies — Detect when your articles are republished without attribution
- Legal teams — Prove content provenance in licensing disputes
- Research organizations — Track how your findings are cited and reused
- Content platforms — Automatically flag potential content reuse at scale
Next steps
Embed & Detect walkthrough
Step-by-step guide with code examples.
Best practices
Production tips for scoring, filtering, and auditing.