> ## 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.

# Overview

> How Factagora Search API delivers source-backed, fact-grounded answers to AI

## 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Traditional search APIs and RAG systems return raw document chunks, including outdated, unsourced, or time-disjoint information.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.

## How it works

<Steps>
  <Step title="Structure">
    Content is decomposed into Factblocks, the smallest verifiable unit of knowledge, each tagged with a source, confidence score, and timestamp.
  </Step>

  <Step title="Connect">
    Facts are linked through the **Temporal Knowledge Graph (TKG)**: which tracks both semantic relations and how those relations evolve over time.
  </Step>

  <Step title="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.
  </Step>
</Steps>

## The 6 APIs

<CardGroup cols={2}>
  <Card title="Fact Search" icon="newspaper">
    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.**
  </Card>

  <Card title="Fact Checker" icon="circle-check">
    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.**
  </Card>

  <Card title="Evidence Hunter" icon="magnifying-glass">
    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.**
  </Card>

  <Card title="Deep Research" icon="book-open">
    Cross-verifies a topic across many sources and synthesizes the timeline and key issues. Fits research automation and AI Analyst workflows. **2 credits.**
  </Card>

  <Card title="Timeseries" icon="chart-line">
    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.**
  </Card>

  <Card title="Causality Graph" icon="diagram-project">
    Analyzes cause-and-effect relationships between facts and produces a causality graph. Surfaces the connection structure between events, not just matches. **3 credits.**
  </Card>
</CardGroup>

## Key capabilities

<CardGroup cols={2}>
  <Card title="Factblocks structure" icon="cubes">
    Information is broken into the smallest verifiable units, each independently sourced and scored.
  </Card>

  <Card title="Temporal awareness" icon="clock">
    Every fact carries time context, so AI knows whether the information is current or stale.
  </Card>

  <Card title="Source-backed" icon="link">
    Every result is traceable to its original source, with credibility scoring applied.
  </Card>

  <Card title="Causality-aware" icon="diagram-project">
    Beyond keyword search, results expose *why* facts connect, supporting reasoning, not just retrieval.
  </Card>
</CardGroup>

## Where Factagora fits

Factagora sits on top of your existing stack — any LLM, any vector DB, any RAG pipeline — as a grounding and verification layer. It **complements** vector search rather than replacing it: embeddings find what's *similar*, while Factagora verifies what's *true right now* and *why*.

* **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**

## Next steps

<CardGroup cols={2}>
  <Card title="API Catalog" icon="grid-2" href="/guides/factagora/search-api/api-catalog">
    Compare the 6 APIs side by side, purpose, credits, and example responses.
  </Card>

  <Card title="API Reference" icon="code" href="/api-reference/introduction">
    Endpoint specs and best practices.
  </Card>
</CardGroup>