Connect trusted sources
Identify documents, knowledge bases and systems worth connecting. Define ingestion, updates, retention and ownership for each source.
KNOWLEDGE, CONNECTED
Help staff find answers in your internal documents.
For organisations where staff spend too long searching policies, manuals and shared documents. We connect approved sources to an AI assistant using enterprise RAG, with source citations, access-aware retrieval and testing against real staff questions.
WHAT WE BUILD TOGETHER
Respect access boundaries
Design retrieval around user identity and source permissions, with document provenance and citations wherever supported.
Evaluate before scaling
Test retrieval quality, unsupported answers and permission boundaries against real questions. Give users a way to flag gaps and improve the knowledge base.
UNDERSTAND THE ENGAGEMENT
What it is.
How we deliver it.
What you are buying
Retrieval-augmented generation connects a model to relevant evidence at answer time. Enterprise delivery adds the source ownership, permissions, evaluation and operating controls needed for company knowledge. It helps staff locate answers without treating model training as a replacement for current documents.
Where it is useful
Start with a collection whose answers matter and whose owner can resolve ambiguity: service manuals, approved policies or product documentation. Teams with inconsistent or outdated material may first need a source-cleanup stage. Retrieval quality depends on the evidence available.
What a delivery scope can include
The scope can include connectors, text extraction, indexing, permission-aware retrieval, citations and the user interface. We define update, deletion and retention behaviour. Private inference and storage can be included where required, subject to model and connector suitability.
What to bring to discovery
- Approved documents and named source owners
- Real staff questions with expected evidence
- Source permissions and identity groups
- Update frequency, retention and deployment constraints
How we evaluate the result
Test supported answers, missing evidence, contradictory versions and restricted documents. Verify that revoked access and deletions propagate. Measure successful tasks and verification time, and keep feedback connected to the source owner.
Decisions to make early
RAG reduces some knowledge gaps but does not eliminate incorrect answers. Citations need checking, especially for consequential decisions. Live transactional facts may require a database or API rather than document retrieval alone.
BUILD YOUR UNDERSTANDING
Useful reading before we talk.
Private AI & RAG · 3 MIN
Explore all services ↗What is RAG? A practical guide to AI that uses your company knowledge
Learn how retrieval-augmented generation works, where it helps, and how to test an assistant against your own documents.
Private AI & RAG · 3 MINOn-premises RAG: architecture, permissions and a pilot checklist
Plan a private document assistant from ingestion to inference, including identity, backups, evaluations and data boundaries.
Private AI & RAG · 3 MINRAG vs fine-tuning: which problem are you actually solving?
Compare knowledge retrieval and model adaptation with concrete examples, cost drivers and a decision checklist.
A LITTLE MORE CLARITY
Good questions.
Clear answers.
Does RAG eliminate incorrect answers?+
No. Retrieval can improve grounding, but evaluation, citations, abstention behaviour and human review remain important for sensitive decisions.
Can it run in our own environment?+
Yes, subject to the selected models and connectors. We can design ingestion, vector storage, retrieval and inference for private or on-premises deployment.
DISCUSS YOUR FIRST USE CASE