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The Data Layer for AI Agents
Precision retrieval get 70% smaller context — only relevant chunks, zero noise

80% COST REDUCTION Process 10x more requests with intelligent retrieval

99.5% ACCURACY Flagship model quality at mini model cost

10x MORE REQUESTS Smaller context windows = more throughput

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The Platform

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

Hippo - RAG & Retrieval

AI-powered search & Q&A → Semantic search across documents → Q&A with source citations → 70% smaller context windows

Lexa - Document Parsing

Extract structured data → 12+ file formats → Vector DB ready chunks → Cloud integrations

Account - User Management

Enterprise operations → Authentication & tokens → Usage tracking → User management

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Why Choose Cerevox?

  • 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
  • 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
  • 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

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Get Started in 60 Seconds

pip install cerevox

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}")

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")
Requirements: Python 3.9+ • Get your API key from Cerevox

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Real-World Use Cases

AI Q&A Systems

Build intelligent Q&A over documents with source citations and 80% cost savings

Knowledge Bases

Create searchable knowledge bases with semantic search and RAG retrieval

Financial Analysis

Query 10-K filings, reports, and financial statements with natural language

Legal Research

Search contracts and legal documents with precision retrieval

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Next Steps

Quickstart Guide

Build your first RAG Q&A system in 5 minutes

Hippo - RAG & Retrieval

Complete guide to semantic search and Q&A

Lexa - Document Parsing

Extract structured data from documents

RAG Examples

End-to-end RAG workflow examples
Ready to build? Try our Demo or join our Discord community for support.