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Documentation Index

Fetch the complete documentation index at: https://docs.factagora.com/llms.txt

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What is Factagora Search API?

Factagora Search API is an AI trust infrastructure that reduces hallucinations in generative AI. It solves a fundamental problem: how do you make sure the information your AI consumes is verifiable, source-backed, and still true right now? Traditional search APIs and RAG systems return raw document chunks, including outdated, unsourced, or time-disjoint information. Factagora structures information as Factblocks instead, with each fact linked to a confidence score, source, and temporal context, so AI only consumes what is verifiable at the current point in time.

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How it works

1

Structure

Content is decomposed into Factblocks, the smallest verifiable unit of knowledge, each tagged with a source, confidence score, and timestamp.
2

Connect

Facts are linked through the Temporal Knowledge Graph (TKG): which tracks both semantic relations and how those relations evolve over time.
3

Serve

APIs query the TKG to answer not only “what was found,” but “is it still true, and why”, returning evidence-grounded results with full source attribution.

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The 6 APIs

News Search

Real-time semantic news search. Goes beyond keywords, returns articles with relevance and source credibility scoring. Reliable supply of fresh information for AI agents, chatbots, and research services. 1 credit.

Fact Checker

Verifies a specific claim with a True / False / Uncertain verdict, supporting sources, and a confidence score. Designed for hallucination detection in generative AI output. 2 credits.

Evidence Hunter

Expands evidence behind a claim across articles, documents, reports, and references. Built to answer “why does this claim hold,” not just “what mentions it.” 2 credits.

Deep Research

Cross-verifies a topic across many sources and synthesizes the timeline and key issues. Fits research automation and AI Analyst workflows. 2 credits.

Timeseries

Extracts how information evolves over time from documents, URLs, and news, and structures it as time-series data. Useful for tracking policy, market, and issue shifts. 3 credits.

Causality Graph

Analyzes cause-and-effect relationships between facts and produces a causality graph. Surfaces the connection structure between events, not just matches. 3 credits.

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Key capabilities

Factblocks structure

Information is broken into the smallest verifiable units, each independently sourced and scored.

Temporal awareness

Every fact carries time context, so AI knows whether the information is current or stale.

Source-backed

Every result is traceable to its original source, with credibility scoring applied.

Causality-aware

Beyond keyword search, results expose why facts connect, supporting reasoning, not just retrieval.

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Where Factagora fits

  • Fact verification for AI chatbots and AI agents
  • Freshness layer for RAG-based services
  • AI trust hardening in finance, legal, and media
  • Research and news analysis automation
  • Internal knowledge verification systems for enterprises
  • Source-backed Explainable AI

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Next steps

API Catalog

Compare the 6 APIs side by side, purpose, credits, and example responses.

API Reference

Endpoint specs and best practices.