Private LLMs, permission-aware RAG and AI agents deployed on your own infrastructure, wired into your real systems.
The gap between an AI demo and an AI system in production is almost never the model. It is retrieval quality, permissions, evaluation and the unglamorous work of connecting to systems that were not designed with AI in mind.
We build enterprise AI that runs where your data already lives. That usually means a private or on-premise model, a retrieval layer that filters by the user's permissions before the model reads anything, citations on every answer, and an evaluation set so you can tell whether a change actually helped.
Where a hosted frontier model is genuinely the better tool and the data is not sensitive, we will use one. The architecture we prefer puts a single interface in front of both, so the application never has to care which is answering.
Open-weight models running on your hardware or inside your cloud account, so data never leaves your boundary.
Hybrid search and reranking over your own content, with citations, so answers can be verified rather than trusted.
Access control enforced at query time, before the model reads anything — not filtered out of the answer afterwards.
Assistants that take real actions in your systems through defined tools, with an audit record of what they did.
A real question set with expected answers and sources, so quality is measured rather than assumed.
Structured data pulled out of invoices, forms and scanned records, with confidence scores and human review for the uncertain cases.
We are vendor-neutral. The stack follows the requirement — including the parts of it you already run.
The sectors where this work most often lands, and what specifically changes for each.
Invoices, statements and forms read into structured data with confidence scores, and the uncertain cases routed to a person rather than guessed at. Once extraction is reliable, reconciliation questions can be asked in plain language against records that never leave your network.
Discharge summaries, protocols and case records are exactly the content retrieval handles well, and exactly the content that must never surface to the wrong user. Permissions enforced before the model reads anything is the entire design constraint here.
For departmental work, sending records to a public API is usually not permitted at all — so the question is not whether to run privately but how. Local models over file and notification archives, with citations, so an answer can be verified against the source document.
Standard operating procedures, quality manuals, drawings and maintenance history are dense, rarely read and full of answers. Retrieval over them turns a two-hour search through a folder share into a cited answer, which is most of the value on a plant floor.
Affiliation rules, curriculum documents and circulars accumulate faster than anyone reads them. A retrieval assistant over that corpus answers staff and student queries with a citation, which is the only form of answer worth giving on a compliance question.
Purchase invoices and goods receipts arrive as paper and PDFs and get typed in. Extraction removes the typing and the transcription errors, and catalogue enrichment from supplier documents makes products findable without someone writing descriptions by hand.
Against your real documents, with an evaluation set built from real questions. It ends in a number for retrieval quality, not an impression — and sometimes in a recommendation not to proceed.
We work out what a private model actually needs for your volume, and compare the running cost against a hosted API honestly, before anyone buys a GPU.
One department, one document class, one user group — with permissions enforced from day one rather than added later.
The eval set is re-run when models, documents or prompts change, so quality regressions are caught by a test rather than by a user complaint.
None of it has to be tidy. Discovering that your data is messier than expected is part of the job, not a reason to delay starting.
Understand goals, constraints and success metrics.
Map processes, data and integration surface.
Design intuitive, accessible enterprise interfaces.
Design for scale, security and future backend.
Build in modular, reviewed increments.
Automated, security and acceptance testing.
Ship to cloud, private cloud or on-prem.
Long-term partnership, SLAs and iteration.
Straight answers, including the cases where our answer is that you should not buy this from us.
Yes. Open-weight models on your own GPUs, or inside your own cloud account, with nothing leaving your network. Modern open models handle retrieval, extraction, classification and summarisation — the bulk of real enterprise work — well enough that the trade-off is much smaller than it was two years ago.
By enforcing permissions at retrieval time. The search itself is filtered by the user's access before any document reaches the model, so restricted content is never in the context to begin with. Filtering the answer afterwards is not a security model.
Every answer cites its sources so a human can verify it, and we build an evaluation set of real questions with expected answers early. Without that you are tuning blind — which is how most stalled AI projects got stuck.
Higher to start and lower per query than a hosted API, with the crossover depending entirely on your volume. It is worth doing the actual arithmetic for your usage rather than assuming either direction, and we will do it with you before you buy hardware.
Each page covers local context — the sectors, the constraints and the questions specific to that market.
Describe the problem in your own words. We will tell you what it takes to solve — and whether it is worth solving this way.
