Pilots that never reach production
A demo works in a controlled setting, then stalls because the data, permissions or evaluation needed for real use were never designed.
Service / Generative AI
Generative AI solutions built for real outcomes.
AI agents, copilots and document intelligence built around a real workflow, with the grounding and oversight the work requires.
Talk to an expert ↗What we make possible
Useful AI starts with a narrow, well-understood job.
Most teams do not need a general-purpose assistant. They need a specific piece of work to become faster, clearer or less repetitive. That is where we start: the decision, the document, the support queue or the internal process that is already costing time.
We then design the smallest system that can help. That might be a retrieval-backed copilot, an agent that prepares a draft for review, or a document pipeline that extracts structured information. In every case, the model is one part of a product that includes source content, permissions, evaluation and a human path for disagreement.
The problem
Where this work usually begins.
These are the patterns we see before a project starts. Naming the problem clearly is the first step toward a useful scope.
Answers without evidence
A fluent response is not the same as a trustworthy one. Without grounding and citations, users cannot tell whether to rely on it.
Unclear accountability
When a model contributes to a decision, the team needs to know who reviews it, what they see and what happens when it is wrong.
What we deliver
Capabilities connected to an outcome.
We choose the mix of work based on the problem, not a fixed menu. Every engagement has a clear output and a named owner.
AI copilots
Assistants embedded in an existing workflow, with access to approved material and a clear review step.
AI agents
Bounded automation that can take a small number of actions, with explicit limits and an audit trail.
Document intelligence
Extraction and summarisation for documents that are too varied or too voluminous for manual processing.
RAG systems
A reliable path from approved source content to a grounded answer, including versioning and access control.
How we work
From a focused question to a confident launch.
Discover
Align on the outcome and the constraint.
Shape
Prototype the smallest credible path.
Build
Deliver in clear, reviewable increments.
Evolve
Measure, learn and improve.
What changes
Outcomes worth measuring.
We agree what the work should change before we build, then use that to decide what is working and what needs another look.
Less repetitive reading
Teams spend less time searching, summarising and re-keying information that already exists.
Faster first drafts
Responses, reports and summaries start from a grounded draft instead of a blank page.
Visible quality
Evaluation cases, citations and feedback make it possible to see where the system helps and where it does not.
Questions
Practical answers before we begin.
Do we need a large language model for this?+
Not necessarily. Some problems are better served by search, rules or classical machine learning. We will recommend a model-based approach only when it is the most practical way to handle the variability of the input.
How do you prevent sensitive data from leaking?+
We map the data path, apply access rules to retrieval and avoid sending information to external services unless the contract and the use case allow it. The design should make the permitted path the easy path.
How do you measure whether it works?+
We build a representative evaluation set before launch, including difficult and ambiguous cases. After launch we track correction rates, rework and whether people keep using the feature in their real workflow.