AI RAG Systems — visual representation

Overview

Everything Included in AI RAG Systems.

A RAG (retrieval-augmented generation) system pairs a large language model with a retrieval layer built on your own documents, so answers are grounded in your actual data instead of the model's general training. This dramatically reduces hallucination and lets the AI answer questions specific to your business — policies, product specs, internal knowledge — accurately.

We build the full pipeline: document ingestion and chunking, vector database setup, retrieval logic, and prompt engineering to keep the model's responses grounded and cited. The system can power an internal assistant, a customer-facing chatbot, or an API your own team consumes.

  • Document ingestion & chunking pipeline
  • Vector database setup (embeddings)
  • Retrieval + generation pipeline
  • Source citation in responses
  • Hallucination reduction tuning
  • API or chat interface delivery

Packages

Choose the Right Package.

Starter

$2,750 one-time

A RAG pipeline over a single, well-defined document set.

  • One document set ingested
  • Vector database setup
  • Basic retrieval pipeline
  • Chat or API interface
  • 5-6 weeks delivery
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Enterprise

Custom Quote

Enterprise-scale RAG across departments and data systems.

  • Cross-department knowledge base
  • Custom embedding & retrieval architecture
  • Access control & data governance
  • Ongoing tuning & support retainer
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Need something outside these tiers? talk to us about a custom quote

FAQ

Questions About AI RAG Systems.

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The Enterprise package is built around scope that doesn't fit a fixed tier — larger builds, unusual data, or multi-phase engagements. We scope it with you and provide a fixed quote before any work begins.

AI RAG Systems is a focused engagement scoped specifically to this need, ideal when you don't require the full breadth of our AI Automation service. We're happy to combine it with other work if your needs grow.

Yes — most clients start with a smaller tier and expand scope later as needs grow. Work already delivered carries over toward the new package.

Yes — ongoing maintenance, monitoring and support retainers are available on every package, so what we build keeps performing after launch.

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