> ## Documentation Index
> Fetch the complete documentation index at: https://docs.seynlabs.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Seyn observes how your organisation actually works and turns it into structured, versioned, fully auditable knowledge.

> Wikis describe the work. VectorDBs show your agents what is similar. Seyn observes why work actually happens the way it does, and gives your agents what is useful.

**Seyn** is a knowledge extraction engine. It connects to the systems where your organisation's work already lives (SharePoint, Teams/Slack, email, document archives, operational tools), ingests the raw activity, and extracts the processes, rules, and patterns that actually run the business. The output is a **versioned knowledge library** where every claim traces back, hop by hop, to the raw source records it was derived from.

<Info>
  **These docs are alpha.** Seyn is in active development. Some capabilities described here are still in testing, and every page flags them explicitly. The current state of each feature lives on [Status](/status).
</Info>

***

## The problem

How an organisation really operates is almost never written down. It lives in people's heads, in ten thousand Teams/Slack messages and emails, in the folder structure of a shared drive, in the gap between the official process diagram and what the team actually does on Thursday afternoons.

Search doesn't fix this. Similarity search finds documents that *look like* your question; it can't tell you who approves what, in which order, or why the process forked last quarter. Useful is not the same as similar.

Seyn's answer is extraction, not search:

* **Interviews ask people what they think they do.** Seyn watches what the data says they do.
* **A process map is an opinion.** A process rule with provenance is a claim with receipts.
* **Similar is a guess.** Extracted, reviewed, versioned knowledge is an answer.

## The principle

Seyn is built on three primitives, and everything else composes from them:

| Primitive      | What it is                                                                                              | Read more                          |
| -------------- | ------------------------------------------------------------------------------------------------------- | ---------------------------------- |
| **Events**     | Raw activity normalized into one schema: who did what, to which entity, when.                           | [Events](/platform/events)         |
| **Knowledge**  | What the events mean: processes, rules, and patterns, extracted and versioned.                          | [Knowledge](/platform/knowledge)   |
| **Connectors** | How the activity gets in: read-only ingestion from any system, through prebuilt connectors or your own. | [Connectors](/platform/connectors) |

Connectors bring the work in. Events make it comparable. Knowledge makes it useful. Querying ties them together: one hybrid retrieval pipeline serves chat, agents, and the API, and every answer can show its evidence.

## What you can build on it

<CardGroup cols={2}>
  <Card title="Grounded agents" icon="robot" href="/platform/mcp">
    Give any MCP-capable agent org-scoped access to extracted process knowledge. Your agents stop guessing how your business works.
  </Card>

  <Card title="Auditable answers" icon="link" href="/platform/provenance">
    Every rule traces four hops back to source records. Built for teams where "the model said so" is not an acceptable answer.
  </Card>

  <Card title="Conversational knowledge" icon="message" href="/platform/chat">
    Ask questions in plain language, get cited answers assembled from hybrid retrieval over versioned, human-reviewed knowledge.
  </Card>

  <Card title="Typed integrations" icon="code" href="/sdk-reference">
    A read-only v1 API with TypeScript and Python SDKs for compliance tooling, internal apps, and BI pipelines.
  </Card>
</CardGroup>

## Performance

Numbers we hold ourselves to on real client corpora:

* **Sub-second hybrid retrieval.** Structured, full-text, and semantic signals run concurrently inside one PostgreSQL instance; queries return well under a second at production corpus sizes.
* **Single-call provenance.** The full four-hop audit chain for any rule resolves in one request.
* **Incremental everything.** Delta sync and content-hash deduplication mean re-ingesting an unchanged corpus costs almost nothing, and analysis cost scales with new data, not corpus size.

A public benchmark suite (retrieval quality, extraction accuracy, end-to-end latency) is in preparation and will be published here. Until then, [request access](mailto:support@seynlabs.com?subject=Seyn%20access%20request) and measure on your own data.

## Where to start

<Steps>
  <Step title="Get the mental model">
    Read [Core Concepts](/core-concepts) for the vocabulary, then [Architecture](/platform/architecture) for how ingestion and querying fit together. Fifteen minutes, and every other page will make sense.
  </Step>

  <Step title="Make a real query">
    The [Quickstart](/quickstart) takes you from an API key to a natural-language query against extracted knowledge in under ten minutes.
  </Step>

  <Step title="Check what's live">
    [Status](/status) tells you what's in testing and what's coming, with reasons.
  </Step>
</Steps>

## Stability

The `/v1` API is the stable public contract. Breaking changes ship as `/v2`; additive changes ship within `/v1`, so write integrations to ignore unknown fields. Features marked **In testing** carry no compatibility promise yet.

Seyn is currently in private alpha. To evaluate it on your own data, [request access](mailto:support@seynlabs.com?subject=Seyn%20access%20request).