AI frameworks and agent build
An assistant that answers only from your approved sources, and cites them
Policies, procedures, technical manuals, standards, contracts, product documentation: the knowledge is in the business but scattered across systems nobody can search in one place. A grounded knowledge assistant finds it, answers from it, and shows its sources, so the answer can be trusted and checked.
The most-requested capability in every discovery programme
Ask any function where its time goes and the same answer comes back: looking for things. Which standard applies, what the procedure says, what the contract committed to, which drive supports which encoder, what the last engineer wrote down. In our discovery work, knowledge, documentation and compliance is one of the strongest cross-business themes every time, raised independently in engineering, manufacturing, procurement, sales and service.
It is also where a generic chatbot does the most damage, because an answer that sounds right and is not is worse than no answer. A knowledge assistant has to be grounded: it answers from cited, approved sources or it says it cannot.
From documents to grounded answers
Ingestion and processing
Document parsing for PDFs, Office files, wikis, SharePoint, ticket histories and structured data; a chunking strategy that respects the document's structure; embeddings; entity extraction.
The knowledge layer
A vector index for semantic retrieval and, where relationships matter, a knowledge graph: which products share which components, which parameters relate to which faults, which clauses depend on which. GraphRAG answers the multi-hop questions a plain index cannot.
Retrieval
Query analysis, hybrid retrieval across vector and graph, and context assembly that respects the user's permissions, so nobody sees an answer drawn from a document they cannot open.
Generation with citations
The answer, with the sources it came from and nothing it cannot support. Citation is enforced, not optional, and "I cannot find that in the approved sources" is a valid answer.
Evaluation and feedback
A question set with agreed answers, run on every change; user feedback on every answer; and a review loop that improves the sources as much as the assistant.
Three patterns we build
Internal enterprise search
One place to ask across technical documents, policies and procedures, with answers grounded in the current approved version.
Product and service support
Configuration and troubleshooting help for customers or field engineers, drawn from manuals, parameters and known-fault histories. The customer-facing version carries the transparency and guardrails the EU AI Act expects.
Engineering knowledge capture
Tribal knowledge that leaves with people captured and connected: who solved what, how, and where the record is, so experts are found and solutions are reused.
The assistant is only as good as the sources
The first task in every knowledge-assistant build is not the assistant; it is deciding what counts as an approved source, where it lives, who owns it and how it is kept current. Where documentation is sparse, out of date or contradictory, the assistant will faithfully report that, which is useful information but not the outcome anyone wanted. That is why this framework is so often paired with data mining and requirements work, and why a trusted knowledge foundation is one of the earliest investments we recommend in any roadmap: so much else depends on it.
Weeks to a working pilot
Two weeks of scoping: sources, owners, permissions, the question set and the success measures. Four to eight weeks of build depending on the sources, with the pilot opened to a user group as soon as the question set passes. Your data stays in your environment or in enterprise services with no-training terms; your code stays yours.
About knowledge assistants
What is the difference between RAG and GraphRAG?
Retrieval-augmented generation finds passages similar to the question and answers from them. GraphRAG adds a knowledge graph of entities and relationships so the assistant can answer questions that span several documents, such as "which drives support this motor with that encoder", which plain retrieval gets wrong.
Can it respect our access controls?
Yes, and it must. Retrieval is filtered by the user's permissions before any answer is generated, so the assistant never reveals a document the person could not open themselves.
Does our data leave our environment?
Not unless you choose it. The index can live in your cloud tenancy; model calls go to enterprise endpoints with contractual no-training terms; and the data flows are documented as part of every scope.
How do you measure whether it works?
A question set with agreed answers, built with your subject experts and run on every change, plus user feedback on every live answer. We report accuracy, citation rate and the proportion of "cannot find" answers, which usually points to gaps in the sources.
Find out what your business already knows
Start with the sources. We will tell you how far they will take an assistant and what to fix first.