Enterprise RAG development
Turn approved documents, databases, and knowledge repositories into answers your teams can verify.
BlueMouse.ai designs and builds retrieval-augmented generation systems for organisations whose documents carry consequences: law firms, finance teams, engineering groups, and regulated operations across the UK. We build the ingestion, retrieval, citation, evaluation, and deployment layers needed to move from a chatbot experiment to a dependable knowledge service, and we measure it before anyone relies on it.
What is included
A RAG system is several systems working together. We scope each layer explicitly, so you know what is being built, what it will be tested against, and what you will own when it is done.
Map your repositories, their owners, permissions, and update cycles, and collect the questions people actually ask. Decide whether RAG, OCR, conventional search, or a combination is the right starting point, and write the acceptance criteria before any code.
Connect SharePoint, Google Drive, document management systems, databases, ticketing tools, and internal APIs through authenticated connectors that respect each source's own permissions and refresh on a schedule you set.
Parse native and scanned files, run OCR where needed, and segment content along its real structure: sections, clauses, tables, and headings. Every chunk carries metadata such as version, date, owner, and jurisdiction so retrieval can filter on it.
Combine keyword search for identifiers and exact terms with semantic search for meaning, fuse the two with reciprocal rank fusion, filter by metadata and permissions, and rerank the shortlist with a cross-encoder where precision at the top matters.
Read: why legal RAG needs hybrid searchAnswers are assembled only from retrieved passages. Each cites the document, page, and version it used, and the system declines when the sources do not support a confident response instead of filling the gap.
Identity, source-level access, tenant separation, retention, and audit logging are designed into the index and the query path. A user can only receive evidence from content they are allowed to open.
Review security and governanceA reviewed test set of real questions and expected sources, with automated measurement of retrieval recall, citation precision, answer faithfulness, refusal behaviour, latency, and cost. It runs before launch and again whenever sources or models change.
Private cloud, on-premise GPU, commercial API, or hybrid deployment, with monitoring, feedback capture, and a review cadence that keeps retrieval quality up as the knowledge base grows.
Compare deployment optionsReference architecture
Every engagement is built on the same reference architecture, adapted to your sources, security boundary, and deployment model. The diagram shows the six layers and the two concerns that cut across all of them.
We are not tied to a single stack. Vector search may run on pgvector, Qdrant, OpenSearch, or Azure AI Search; keyword retrieval on OpenSearch or Elasticsearch; embeddings and language models from commercial APIs or open-weight models such as Llama and Mistral served on private GPUs. The choice follows your data boundary, latency, and cost constraints, and we document the reasoning so it can be revisited.
Source code, prompts, evaluation sets, infrastructure definitions, and documentation are delivered into your repositories and environments. There is no proprietary runtime you depend on us for. The evaluation harness we use during the build stays with your team, so you can measure the effect of every later change yourself.
Retrieval design
Most RAG failures are retrieval failures that look like generation failures: a fluent answer built on the wrong passage. These are the decisions we make deliberately, with your documents in front of us, rather than by default.
Delivery process
Six steps, each ending in something you can read, test, or approve. You can stop after any of them with the work so far in hand.
Understand the users, the questions they ask, the sources that should answer them, and the security and deployment constraints. You leave knowing whether RAG is the right tool.
Inspect repositories, formats, permissions, and content quality. Collect representative questions and start the reviewed evaluation set that the whole project will be measured against.
A short proposal naming the document set, the required outputs, the evaluation approach, the deployment boundary, and the acceptance criteria the pilot must meet.
Ingestion, hybrid retrieval, citations, and a working interface on your real documents. Retrieval and answer quality are measured separately against the test set, and weak cases are reviewed with you.
Access controls, integrations, monitoring, and human review paths, released in the environment agreed in the scope: private cloud, on-premise, commercial API, or hybrid.
Monitor relevance, faithfulness, refusals, latency, and cost. Re-run the evaluation set as sources and models change, and adjust chunking, retrieval, or prompts with evidence.
The same six steps apply to every engagement. See how a project runs, including how we decide whether an AI agent is needed at all.
Quality before scale
A fluent answer is not enough. We test whether the system found the right evidence, whether the response is supported by that evidence, and whether it behaves safely when the source cannot answer.
Representative questions are paired with expected sources or evidence. Testing covers relevance, ranking, metadata filters, document freshness, permission boundaries, and difficult terminology before prompt changes can hide retrieval weaknesses.
Review checks focus on citation support, completeness, unsupported claims, appropriate refusal, format, and consistency. Feedback and production traces show where content, retrieval, or generation needs adjustment.
Security and deployment
Identity, source-level access, tenant separation, retention, and logging are designed around the sensitivity of your documents and users, and enforced in the index rather than in the prompt.
Review security and governanceUK-hosted private cloud, on-premise GPU servers, commercial model APIs under data-processing terms you accept, or a hybrid that keeps documents and indexes private while using external models for defined tasks.
Compare deployment optionsUncertain answers, sensitive requests, and consequential actions route to an authorised person. Automation is added only where the workflow needs it, and never as the default.
See controlled AI agentsConnected document intelligence
A RAG service can be designed around shared drives, document management systems, SharePoint, Google Drive, databases, business applications, and authenticated APIs. The right integration depends on source ownership, permissions, freshness, and how users need to work.
Image-only PDFs, scans, forms, and photographed documents need an OCR and document-processing stage. Text, layout, tables, fields, confidence signals, and validation results then enter the index with useful metadata.
Explore OCR document processingWhere we apply it
Retrieval design changes with the documents. These are the workflows we build most often, each with its own page, and a live application you can look at.
Clause-level extraction, playbook comparison, and retrieval pinned to the statute version in force at the matter date, with counsel approving every conclusion.
Legal RAG workflowPurchasing and expense policy retrieved with citations beside each invoice exception, so the reviewer sees the rule and the evidence together.
Finance document workflowManuals, drawings, and inspection reports searched at the current revision, with answers that cite document, page, and revision for an engineer to confirm.
Engineering knowledge workflowReplies drafted from approved knowledge articles filtered by product and plan, shown with their source, and sent only after an agent reviews them.
Support knowledge workflowAdministrative procedures retrieved with citations for intake and claims teams, with special-category data handled deliberately and no clinical decisions.
Healthcare admin workflowThe standard operating procedure for each type of delivery discrepancy retrieved with its source, so the operations owner acts on the procedure rather than on memory.
Operations document workflowTo see a working system rather than a description, the construction knowledge workspace indexes approved project folders and answers questions with grounded context, and builds RICS survey reports section by section with human review before export.
Decision guidance
RAG is the right answer to a specific problem: questions that must be answered from changing or private knowledge, with evidence. It is not the right answer to every problem, and the scoping call is where we say so.
Common questions
PDFs, office files, web content, database records, knowledge platforms, and application data are all candidates when access and update rules are clear. Scanned material joins them after an OCR and validation stage.
Define representative questions and the evidence that should answer them, then measure retrieval and answer behaviour separately before launch and again whenever sources or models change. The evaluation set stays with you.
Three controls together: answers are assembled only from retrieved passages, every claim carries a citation a reviewer can open, and the system declines when the evidence is insufficient. Faithfulness and refusal are measured in the evaluation set, not assumed.
Yes. The architecture can use UK-hosted private cloud, on-premise GPU servers, commercial APIs under data-processing terms you accept, or a hybrid that keeps documents and indexes private while using an external model for defined tasks.
Yes. Image-only PDFs, scans, and photographed documents pass through OCR and validation first, so text, tables, and page references enter the index with confidence signals and a path back to the original page.
The smallest one that passes your evaluation set at the required quality. That may be a commercial API, an open-weight model on private GPUs, or a mix routed by query class. We document the choice and the cost per query.
Document volume and variety, the number and freshness of sources, permission complexity, integrations, evaluation depth, and the deployment model. We scope one workflow first and quote against written acceptance criteria rather than a package.
Source readiness matters more than engineering. A pilot on one document family with clear owners and permissions moves quickly; a broad corpus with unclear access rules does not. The written scope sets the timeline explicitly.
Everything: code, prompts, evaluation sets, infrastructure definitions, and documentation, delivered into your environments. Managed optimisation is available as an ongoing service, but it is optional, not a dependency.
Start with one knowledge workflow
We will help define a focused RAG opportunity, the evidence needed to evaluate it, and a practical route from pilot to production, and we will say plainly if RAG is not the right tool.