Knowledge Swarms are **AI knowledge engines** tailored to your project’s unique data. It aggregates, synthesizes, and updates information from public/private sources (news, on-chain activity, social media, docs) into a unified system accessible via APIs, AI agents, or applications.
**Think of it as:**
* A self-updating, high-context “brain” for your project or topic.
* A scalable alternative to static knowledge bases or brittle RAG pipelines.
Built on [Fractal](/fractal.md), our proprietary knowledge engine that removes redundancy and ensures real-time accuracy.
## How It Works
***
1. **Define Scope:**
* Identify your data needs and sources for ingestion
* For example: Learn Pudgy Penguins ecosystem updates, US stock news, or BNB Chain developer activity.
2. **Pipeline Setup:**
* Public Sources: News, social media, blockchain transactions.
* Private Sources: Internal docs, proprietary APIs.
* New Sources: Integrate new APIs or sites.
3. **Fractal Integration**
* [Fractal](/fractal.md) groups related data into subgraphs, e.g. all Pudgy Penguin trades + related tweets + lore updates.
* Updates subgraphs in real-time as data changes.
4. **Model Training**
* Your AI model connects to the knowledge engine, learning from Fractal’s structured subgraphs.
5. **Deployment**
* Powers your AI models and [Universal Agents](/launch/universal-agents.md)
* Which in turns, powers any front-end: chatbots, AI agents, games, analytics dashboards, and more.
*Typical integration time: 3–7 days.*
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[Contact us](https://t.me/alphakek_chat) or [apply here](https://forms.gle/1qX76A98uUb6B9JV9) to build your own Knowledge Swarm.
{% endhint %}
## Why It's Unique
***
**For Crypto Projects:** Fractal natively handles overlapping on-chain and off-chain data, e.g. linking a token’s price drop to Reddit speculation + exchange outflows.
**Sustainability:** Reduces token usage by 30–50% vs. traditional RAG by eliminating redundant data.
**Real-Time Context:** Automatically update answers when new data arrives, e.g. notify users if a protocol hack impacts their query.
**New Data Pipelines:** We adapt to your sources, whether scraping forums or parsing onchain transactions.
**Enterprise-Ready:** Deploy outputs as APIs, AI agents, or embed into existing apps via [Universal Agents](/launch/universal-agents.md).
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# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.alphakek.ai/launch/knowledge-swarms.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.