RAG & Knowledge Systems
Your data, retrievable.
Retrieval-augmented generation systems that ground AI in your docs, data, and institutional knowledge.
Starting range to help you plan. Final scope and quote depend on requirements — media spend, talent, and licenses are separate.
— Overview
RAG is how you make AI useful with your data. We design and build retrieval pipelines — ingestion, chunking, embedding, vector search, reranking, and generation — that work on real document corpora and stay accurate as content changes.
Deliverables include the full RAG stack, ingestion pipelines that keep up with new content, evaluation harnesses to track quality over time, and admin tools so your team can manage sources and permissions.
— Capabilities
What's in scope.
— Outcomes
What you get.
- Answers grounded in your truth, not the open web
- A system that stays accurate as your content evolves
- Quality you can measure and improve
What AI handles
- Auto-classify and tag incoming documents
- Suggest chunking and prompt strategies
- Generate eval cases from real queries
What humans handle
- Choose the embedding and retrieval architecture
- Define what counts as a correct answer
- Own access control and data governance
— Deliverables
— Also known as
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— FAQ
Common questions.
- Can you keep RAG current as content updates?
- Yes — we build incremental ingestion pipelines tied to your CMS, file storage, or data sources, with reindexing on schedule or webhook.
— Related services
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