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

# Overview

<p style={{fontSize: '1.2em', margin: '1em 0'}}>
  <strong>The Data Layer for AI Agents</strong><br />
  Precision retrieval get 70% smaller context — only relevant chunks, zero noise
</p>

<CardGroup cols={3}>
  <Card icon="chart-line" color="#16a34a">
    **80% COST REDUCTION**
    Process 10x more requests with intelligent retrieval
  </Card>

  <Card icon="bullseye" color="#0285c7">
    **99.5% ACCURACY**
    Flagship model quality at mini model cost
  </Card>

  <Card icon="rocket" color="#ea5a0c">
    **10x MORE REQUESTS**
    Smaller context windows = more throughput
  </Card>
</CardGroup>

## The Platform

**Cerevox provides three powerful APIs for building AI agent data infrastructure:**

<CardGroup cols={3}>
  <Card title="Hippo - RAG & Retrieval" icon="hippo" color="#0285c7" href="/hippo/overview">
    **AI-powered search & Q\&A**
    → Semantic search across documents
    → Q\&A with source citations
    → 70% smaller context windows
  </Card>

  <Card title="Lexa - Document Parsing" icon="file-lines" color="#16a34a" href="/lexa/overview">
    **Extract structured data**
    → 12+ file formats
    → Vector DB ready chunks
    → Cloud integrations
  </Card>

  <Card title="Account - User Management" icon="users" color="#ea5a0c" href="/account/overview">
    **Enterprise operations**
    → Authentication & tokens
    → Usage tracking
    → User management
  </Card>
</CardGroup>

## Why Choose Cerevox?

<AccordionGroup>
  <Accordion icon="brain" title="Intelligent Retrieval - 80% Cost Savings">
    * **Precision RAG** - Only retrieve relevant chunks, eliminate noise
    * **70% smaller context** windows mean massive cost reduction
    * **99.5% accuracy match** to flagship models at mini model cost
    * **Smart chunking** optimized for semantic search and embeddings
  </Accordion>

  <Accordion icon="bolt" title="Enterprise Performance & Scale">
    * **10x faster** than traditional solutions
    * **Native async support** across all APIs (Hippo, Lexa, Account)
    * **Enterprise-grade reliability** with automatic retries and error handling
    * **Batch processing** for thousands of documents
  </Accordion>

  <Accordion icon="plug" title="Complete Integration Ecosystem">
    * **Vector database ready** - Works with Pinecone, Weaviate, Chroma, etc.
    * **7+ cloud storage** integrations (S3, SharePoint, Google Drive, Box)
    * **Framework agnostic** - Django, Flask, FastAPI, LangChain
    * **Production ready** with comprehensive error handling and monitoring
  </Accordion>
</AccordionGroup>

## Get Started in 60 Seconds

<CodeGroup>
  ```bash Installation theme={null}
  pip install cerevox
  ```

  ```python Hippo - AI Q&A System theme={null}
  from cerevox import Hippo

  # Initialize Hippo client
  hippo = Hippo(api_key="your-api-key")

  # Create a knowledge base folder
  folder = hippo.create_folder("Product Documentation")

  # Upload documents
  hippo.upload_file(folder.id, "user-guide.pdf")
  hippo.upload_file(folder.id, "api-docs.pdf")

  # Create a chat session
  chat = hippo.create_chat(folder.id, "Technical Support")

  # Ask questions and get answers with citations
  answer = hippo.submit_ask(chat.id, "How do I authenticate users?")
  print(f"Answer: {answer.response}")
  print(f"Sources: {answer.sources}")
  ```

  ```python Lexa - Document Parsing theme={null}
  from cerevox import Lexa

  # Initialize Lexa client
  client = Lexa(api_key="your-api-key")

  # Parse documents into structured data
  documents = client.parse(["invoice.pdf", "report.docx"])

  # Get vector DB optimized chunks
  chunks = documents.get_all_text_chunks(target_size=500)
  print(f"Ready for embedding: {len(chunks)} chunks")
  ```
</CodeGroup>

<Note>
  **Requirements:** Python 3.9+ • [Get your API key](https://cerevox.ai/lexa) from Cerevox
</Note>

## Real-World Use Cases

<CardGroup cols={2}>
  <Card title="AI Q&A Systems" icon="messages-question" href="/usecases/ai-qa-systems">
    Build intelligent Q\&A over documents with source citations and 80% cost savings
  </Card>

  <Card title="Knowledge Bases" icon="brain" href="/usecases/knowledge-bases">
    Create searchable knowledge bases with semantic search and RAG retrieval
  </Card>

  <Card title="Financial Analysis" icon="chart-line" href="/usecases/financial">
    Query 10-K filings, reports, and financial statements with natural language
  </Card>

  <Card title="Legal Research" icon="scale-balanced" href="/usecases/legal">
    Search contracts and legal documents with precision retrieval
  </Card>
</CardGroup>

## Next Steps

<CardGroup cols={2}>
  <Card title="Quickstart Guide" icon="play" href="/welcome/quickstart">
    Build your first RAG Q\&A system in 5 minutes
  </Card>

  <Card title="Hippo - RAG & Retrieval" icon="hippo" href="/hippo/overview">
    Complete guide to semantic search and Q\&A
  </Card>

  <Card title="Lexa - Document Parsing" icon="file-lines" href="/lexa/overview">
    Extract structured data from documents
  </Card>

  <Card title="RAG Examples" icon="laptop-code" href="/examples/rag-workflow">
    End-to-end RAG workflow examples
  </Card>
</CardGroup>

***

<Tip>
  **Ready to build?** Try our [Demo](https://cerevox.ai/lexa) or join our [Discord community](https://discord.gg/cerevox) for support.
</Tip>