AI Integration & RAG

RAG is a technique that connects an LLM (like GPT-4) to your business's private knowledge base. Instead of relying on its pre-trained knowledge, the model retrieves relevant documents from your database and uses them as source context to answer user queries, ensuring factual accuracy and eliminating hallucinations.

We prioritize enterprise-grade security. We configure private endpoints, utilize VPCs, and integrate models with strict IAM roles. We recommend using enterprise AI APIs (which do not train on customer data) or deploying open-source models inside your own secure cloud environment (AWS/GCP).

A Proof of Concept (PoC) RAG system using your data can be designed, built, and deployed in 3 to 4 weeks. Full enterprise integration, which includes automatic document sync pipelines, fine-tuned retrieval ranking, and user access controls, usually takes 8 to 12 weeks.

Yes. We build custom ETL document ingestion pipelines that use OCR and layout detection to extract text, tables, and images from complex document types. We then convert them into clean markdown structures before embedding them into the vector database.

We set up event-driven database synchronization. Whenever a file is added, edited, or deleted in your storage (e.g. S3, Google Drive, Sharepoint), a background serverless function is triggered to re-parse, re-embed, and update the vector index in real time.