Retrieval-Augmented Generation
Platform for Knowledge & Support
Build production-ready RAG systems to power AI knowledge bases, document search, and customer support. Available as a dashboard and API-first SaaS. Pay only for what you use.
How It Works
From raw documents to AI-powered answers in three steps.
Ingest Documents
Upload PDFs, Word documents, Notion exports, Confluence pages, or support tickets. Automatic chunking and embedding generation using state-of-the-art models.
Build Your Knowledge Base
Documents are semantically indexed using vector embeddings and stored in isolated, secure vaults. Each vault is a self-contained AI knowledge base.
Query with AI
Ask questions in natural language. Get precise answers with citations to source documents. Retrieval-augmented generation eliminates hallucination by grounding responses in your data.
RAG for Knowledge Management
Turn your company's scattered documentation into a searchable AI knowledge management platform. Semantic search across internal docs, SOPs, wikis, and knowledge bases — find answers in seconds instead of hours.
- Enterprise knowledge search across PDFs, Confluence, Google Drive
- AI document retrieval with semantic understanding
- LLM-powered knowledge base for internal teams
- Search across SOPs and internal documentation instantly
"What is our refund policy for enterprise customers?"
Enterprise customers are eligible for a full refund within 30 days of purchase. After 30 days, a prorated refund is available...
RAG for Customer Support
Deploy an AI customer support chatbot grounded in your actual documentation. Deflect tier-1 tickets, enable 24/7 self-serve support, and reduce support ticket volume — without hallucinating answers.
- AI agent for tier-1 support with documentation chatbot
- Reduce support tickets using AI-powered self-serve
- Support knowledge base AI with 24/7 availability
- Answers grounded in your docs — zero hallucination
API-First RAG Platform
Build RAG into your own products with our REST API. Scoped API keys, usage tracking, and structured responses. Everything you need to integrate retrieval-augmented generation into any application.
- RAG API with scoped permissions and usage metering
- Semantic search API for vector and keyword retrieval
- Document ingestion pipeline via API
- Developer-first platform with full API documentation
curl -X POST /vaults/{id}/query \
-H "Authorization: Bearer <token>" \
-d '{
"question": "What is our SLA?"
}'{
"answer": "The standard SLA
guarantees 99.9% uptime...",
"sources": [
{ "document": "sla.pdf",
"relevance": 0.94 }
]
}Built for Production
Enterprise-grade features for teams that need reliable, scalable AI.
Multi-Format Ingestion
Upload PDFs, Word documents, text files, and more. Automatic text extraction, intelligent chunking, and embedding generation.
Semantic Document Search
Vector search powered by pgvector finds semantically relevant content, not just keyword matches. Hybrid retrieval for maximum accuracy.
Cited Answers
Every AI response includes citations to source documents. Know exactly where each answer comes from. Full auditability.
Isolated Data Vaults
Each vault is a self-contained knowledge base with row-level security. Your data never leaks across tenants or users.
Credit-Based Pricing
Pay only for queries you run and documents you process. No monthly minimums, no seat-based pricing. Top up when you need to.
Usage Tracking
Full audit log of every API request. Track usage per API key, monitor costs, and understand how your knowledge base is being used.
Enterprise-Grade RAG Architecture
A production RAG system built on proven infrastructure, not a demo.
Document Processing
- Chunking and embeddings
- OpenAI text-embedding-3-small
- Configurable chunk sizes
Vector Storage
- PostgreSQL + pgvector
- Cosine similarity search
- Per-vault isolation with RLS
LLM Generation
- Multiple LLM models supported
- Context-aware generation
- Source-grounded responses
Infrastructure
- AWS Lambda (serverless)
- API Gateway (REST)
- S3 document storage
Why RAG Instead of Fine-Tuning?
Retrieval-augmented generation is the production-proven approach for knowledge-grounded AI.
| RAG | Fine-Tuning | |
|---|---|---|
| Data freshness | Real-time updates | Requires retraining |
| Cost | Pay per query | Expensive training runs |
| Accuracy | Cites sources, verifiable | Can hallucinate |
| Setup time | Minutes | Days to weeks |
| Data privacy | Data stays in your DB | Sent to training pipeline |
| Scalability | Add documents anytime | Retrain on new data |
Your Data Stays Private
Built with enterprise security requirements from day one.
No Training on Your Data
Your documents are used for retrieval only. We never train models on customer data. Embeddings are isolated per user.
Encryption & Isolation
End-to-end encryption in transit. Row-level security ensures complete tenant isolation. Each vault is a separate security boundary.
Full Audit Trail
Every API request is logged with request ID, timestamp, and resource accessed. Complete auditability for compliance requirements.
Start building your AI knowledge base
Upload documents, create vaults, and query with AI in minutes. Credit-based pricing — pay only for what you use.