Turn your data into
grounded intelligence.
Enterprise RAG architectures, custom vector search, and private LLM fine-tuning — no hallucinations, full data privacy.
"Summarize Q3 cloud expenses"Embedding Match (pgvector) -> Top 3 Docs"Cloud spend was $42,500 [Doc #12]"Production LLM engineering.
Enterprise RAG Pipelines
Connect LLMs safely to internal knowledge bases, drive document Q&A, and eliminate hallucination.
LLM Fine-Tuning
Adapt Llama 3, Mistral, or Qwen to your specific domain jargon, tone, and structured JSON output formats.
Vector Search & pgvector
High-performance vector database setup, hybrid search (keyword + semantic), and Cohere reranking.
Private LLM Deployment
Self-hosted open weights models inside your own VPC (vLLM, Ollama, TGI) for SOC2 and HIPAA compliance.
RAG Evaluation (Ragas)
Automated benchmark suites monitoring groundness, relevance, and hallucination rates in real time.
Multi-Modal AI
Process images, charts, and structured tables alongside textual documents using Vision LLMs.
Zero hallucination guarantee.
Data Ingestion & Chunking
Connecting to PDFs, SQL databases, Notion, and Slack — creating semantic chunks with optimal token windows.
Embeddings & Vector Indexing
Generating high-dimensional embeddings and indexing them into pgvector, Qdrant, or Pinecone for ultra-fast cosine similarity search.
RAG Pipeline & Reranking
Hybrid search (dense + sparse), Cohere reranking, and prompt synthesis to eliminate hallucinations and retrieve grounded context.
Evaluation & Fine-Tuning
Continuous evaluation using Ragas framework, latency optimization, and optional domain fine-tuning (Llama 3 / Mistral).
Common questions.
RAG (Retrieval-Augmented Generation) grounds LLMs on your company internal data in real time. It prevents hallucination and ensures responses are secure and accurate.
Yes! We deploy open-source models (Llama 3, Mistral) on your AWS/GCP VPC or local GPU hardware so your data never leaves your network.
We use advanced multimodal parsing (Unstructured, LlamaParse) combined with hybrid search to preserve table structure and image context.
pgvector (PostgreSQL), Pinecone, Qdrant, Weaviate, and Milvus. We recommend pgvector for easy stack maintenance or Qdrant for massive scale.
Ready to query your company knowledge?
We build a proof-of-concept RAG system on a sample of your documents in under 5 business days.