Enterprise RAG

Your knowledge base, actually searchable

Citation-backed AI trained on your internal documents. On-premise or private cloud. Zero hallucination risk on factual retrieval. Built for data-sensitive industries.

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Price anchor: $15K – $35K per system · $3.70 ROI per $1 invested

Production RAG capabilities

  • Hybrid search: dense (vector) + sparse (BM25) retrieval
  • Per-document citation with source attribution on every response
  • Namespace isolation for multi-tenant deployments
  • On-premise deployment, keeping all data inside your infrastructure
  • Quantized local LLMs (Qwen3.5 Q4_K_M, Mistral, Llama 3) for air-gapped environments
  • Re-ranking pipeline for precision improvement
  • Chunking strategy tuned to your document type (PDF, HTML, code)
  • Ingestion pipeline for incremental updates without full re-index

Tested model configurations

Qwen3.5 4B Q4_K_M25–40 tok/s
VRAM: 4–6 GBBest local model for most deployments
nomic-embed-textFast batch embed
VRAM: 274 MBEmbedding model with open weights
GPT-4o / Claude 3.5~50 tok/s
VRAM: API (cloud)For cloud deployments

Frequently asked questions

What is a RAG system?

Retrieval-Augmented Generation (RAG) is an architecture that retrieves relevant documents from your knowledge base and passes them as context to an LLM before generating a response. This grounds answers in your actual data instead of the model's training weights, and enables citation.

Can this run completely on-premise?

Yes. We deploy quantized open-weight LLMs (Qwen, Mistral, Llama) locally using Ollama or vLLM, combined with a self-hosted Qdrant instance. A 6GB VRAM GPU can run a full production stack at 25–40 tokens/sec.

How do you prevent hallucinations?

Retrieval-grounding, forced citation, constrained generation (the model can only answer from retrieved context), and confidence thresholds that route low-confidence queries to a human fallback.

How long does ingest take for a large document corpus?

Typical ingest rates: ~500 to 2000 pages/minute with parallel chunking and batch embedding. A 10,000-document corpus typically ingests in under 2 hours. Incremental updates (new documents only) are near-real-time.

Built for your industry

Insurance & Financial Services
Claims agent · Policy RAG · CCHI / PDPL aligned

Need a private knowledge assistant?

We'll scope the document corpus, recommend the right embedding and retrieval strategy, and deliver a production system with full source code.

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