RAG Knowledge Systems
RAG (Retrieval-Augmented Generation) Knowledge Systems give your team private, natural-language Q&A over your own documents, wikis and knowledge bases, with answers that cite their sources and respect who is allowed to see what.
What it is
We index your content, retrieve the most relevant passages for each question, and use a language model to compose an answer grounded in those passages. Every answer links back to its sources, and permissions from your existing systems are enforced so users only see content they already have access to.
Who it is for
- Knowledge management and IT teams
- Customer support teams
- Legal and operations teams
What we deliver
- Connectors and ingestion pipelines for your document sources
- Search and retrieval tuned to your content
- Answer generation with source citations
- Access control aligned with your existing permissions
- A chat or search interface, or an API for your own apps
- Evaluation, monitoring and handover documentation
Example use cases
- Internal policy and procedure Q&A
- Support agents finding answers in product documentation
- Searching contracts and operational playbooks
- Onboarding new staff with a searchable knowledge base
How it fits with Agentic AI and AWS / Azure AI
RAG is the knowledge layer for Agentic AI agents and AI Assistants, letting them act on accurate, current information. We can build it on AWS / Azure AI services within your own cloud environment.
FAQ
Will our data stay private?
The system is designed to run within environments you control, and access control limits answers to content each user is allowed to see.
How do we know answers are accurate?
Answers include citations to source documents so users can verify them, and we evaluate quality against real questions during the build.
What sources can it use?
Typical sources include file shares, wikis, ticketing systems and document repositories. We confirm connectors during scoping.
Talk to us
Want reliable answers from your own knowledge? Contact us.