Connect your AI to your business data. We build RAG systems that give LLMs accurate, up-to-date answers from your documents, databases, and APIs — in production.
Understanding RAG
RAG (Retrieval-Augmented Generation) is an architecture that connects an LLM to your proprietary data. Instead of relying only on its training data, the LLM retrieves relevant information from your documents, databases, or APIs before generating a response — giving you accurate, citation-backed answers grounded in your actual business data.
You need RAG when your AI must answer questions about internal knowledge bases, company policies, product catalogs, legal documents, medical records, or any data that wasn't in the model's training set. If your users ask 'What does our policy say about X?' or 'Find me the relevant clause in contract Y' — that's a RAG use case.
Capabilities
Decision Guide
Your data changes frequently (documents, knowledge bases, product catalogs). You need citation-backed answers with source references. You want to keep using a general-purpose LLM but make it answer from your data. RAG is faster to build and easier to update.
Best for: Dynamic data, compliance, knowledge bases, customer support
You need the model to learn a specific writing style, domain vocabulary, or specialized reasoning. Your data is stable and won't change often. You need lower per-query latency and cost at high volume. Fine-tuning changes the model itself.
Best for: Specialized tone, high-volume processing, domain expertise
Tech Stack
Process
We analyze your data sources, document types, update frequency, and quality to design the optimal ingestion and chunking strategy.
Vector database selection, embedding model choice, retrieval strategy, and re-ranking approach — all documented in a fixed-scope proposal within 72 hours.
Senior engineers build the pipeline iteratively. Weekly accuracy benchmarks, retrieval quality testing, and live demos throughout.
Production deployment with monitoring dashboards, accuracy tracking, cost optimization, and optional ongoing maintenance.
Investment
All RAG projects are fixed-scope. The price you agree to is the price you pay — no hourly billing, no surprise invoices.
6–10 weeks
Single data source (e.g., PDF knowledge base, help docs). Includes ingestion pipeline, vector database, retrieval, LLM integration, and basic evaluation.
10–20 weeks
Multiple data sources (documents, databases, APIs, Slack, email). Advanced chunking, hybrid search, re-ranking, permissions/access control, and comprehensive accuracy benchmarking.
RAG connects an LLM to your proprietary data — documents, databases, APIs — so it can answer questions accurately using your business information instead of only its training data. Think of it as giving the AI a searchable library of your company's knowledge.
We recommend Pinecone for managed simplicity and fast scaling, Weaviate for hybrid search (keyword + semantic), and pgvector if you want to keep everything in PostgreSQL. We help you choose based on your data volume, query patterns, and operational preferences.
Well-built RAG systems achieve 85–95% accuracy on domain-specific questions. We set up evaluation pipelines to measure retrieval quality and answer accuracy continuously, and optimize chunking, embedding, and re-ranking to improve results over time.
Yes — that's one of RAG's biggest advantages over fine-tuning. We build incremental ingestion pipelines that process new and updated documents automatically, so the AI always has access to your latest data.
Related Case Study
Built an LLM pipeline with OCR, classification, extraction, and validation — replacing 40+ hours/week of manual document review per analyst.
$200K/year saved · 95% accuracy
View Case StudyTell us about your data and use case. We'll send a fixed-scope RAG development proposal in 72 hours.