Keep your intellectual property within your own perimeter. We deploy production-grade RAG systems using local LLMs and vector databases, eliminating data leaks to public AI providers.
Your private data never leaves your environment. No training on your docs by public LLMs.
Quantized local models running on specialized hardware or secure cloud vPC instances.
Native connectors for SharePoint, Slack, PDF, and SQL without intermediate cloud storage.
Retrieval-Augmented Generation (RAG) is not just a database search. It's a complex pipeline requiring precision at every stage. We build end-to-end pipelines optimized for accuracy and low latency.
Semantic chunking and recursively optimized document splitting for high context retention.
Hybrid retrieval combining BM25 keywords with vector embeddings for 99% accuracy.
Continuous monitoring of faithfulness, answer relevancy, and context precision.