Custom RAG Pipelines
Chat with your business data safely. We build enterprise-grade retrieval systems using vector databases and semantic search, ensuring your AI knows exactly what your business knows.
Production Deployment
We don't just hand over a notebook. We deliver fully containerized services (Docker/Kubernetes) with established CI/CD pipelines, automated testing, and comprehensive monitoring to ensure 99.9% uptime.
SOC2 Compliant
Security is baked in, not bolted on. Our architectures adhere to SOC2 Type II standards, featuring end-to-end encryption, strict Role-Based Access Control (RBAC), and immutable audit logs for all AI interactions.
Fixed-Price Sprints
Budget certainty is key. We operate on a fixed-price, outcome-based engagement model. You know exactly what features will be delivered and at what cost, with zero risk of hourly billing creep.
How It Works
Retrieval-Augmented Generation (RAG) connects your proprietary data—PDFs, SQL databases, internal Wikis—to Large Language Models. We implement a robust ingestion pipeline that chunks, embeds, and indexes your data into a vector store (like Pinecone, Milvus, or pgvector).
When a user asks a question, our system retrieves the most relevant context and feeds it to the LLM, ensuring answers are accurate, grounded, and citation-backed.