Service
AI and Automation
An AI demo takes an afternoon. An AI system that is accurate, safe and cheap enough to run every day takes real engineering. We build AI agents, document processing, intelligent customer service and automated enterprise workflows on solid data foundations, with evaluation and guardrails from the start, so what you deploy is something people lean on rather than work around.
- Evaluated, not vibes
- Guardrails and fallbacks
- Grounded in your data
Every change
Scored against an evaluation set before release
2 layers
Input and output guardrails on every AI feature we ship
Vector + RAG
Retrieval over your data, not the open web
What you get
Outcomes, not activity
Every engagement is measured against these. If we are not moving them, we are not doing our job.
Automation that holds
Measured accuracy, sensible failure modes and a deterministic fallback, so a bad model day does not become a bad business day.
Work that disappears
Repetitive processing, triage and data entry handled by the system, so people spend their time on judgement.
Costs you can predict
Model choice, caching and routing tuned so inference cost scales with value, not with usage spikes.
What we do
AI & Automation capabilities
AI Agents
Goal-driven agents that plan, call your tools and act across multiple steps, with the tool layer, checkpoints and approval points engineered around them.
Business Automation
End-to-end automation of a process: intake, classification, routing, action and exception handling.
Intelligent Customer Service
Assistants that resolve real requests using your knowledge and systems, and hand off cleanly when they should.
Document Processing
Extraction, classification and validation of invoices, forms, contracts and records, with confidence scores and human review where it matters.
AI-powered Enterprise Workflows
AI as a step inside a larger workflow: drafting, summarising, checking and prioritising, with a person in control of the outcome.
How we work
Our approach to ai & automation
The practices that make the outcomes above repeatable rather than lucky.
Evaluation-driven
Every prompt, model and retrieval change is scored against a fixed test set, so a regression never ships by accident.
Grounded in Your Data
Retrieval-augmented generation over your content, with strict prompt boundaries and citations, so answers are traceable.
Deterministic Fallbacks
When confidence is low or a guardrail fires, the system falls back to a defined, predictable path rather than guessing.
Human-in-the-loop
The AI drafts and the person decides wherever the stakes justify it.
Technologies
What we build with
A starting point, not a fixed menu. Each links to how we work with it.
Engagement models
How we work together
Pick the shape that fits the work. We will tell you if you have picked the wrong one.
Embedded team
A cross-functional squad that plugs into your organisation, joins your standups and owns a slice of the roadmap. Best when the work is ongoing.
Fixed-scope project
A defined outcome, a fixed budget and a firm date. Best for a launch, a rebuild or a well-understood piece of work.
Advisory and audit
A senior review of architecture, delivery or a specific decision, with a written report and a prioritised action list. Best when you need direction fast.
Related
Services that go with this
Software Engineering
Custom applications and platforms, engineered to last and built for your team to own.
Digital Transformation
Bring fragmented operations into systems your organisation actually runs on.
Infrastructure & Security
The secure foundation your applications run on, designed in from the start.
FAQ
AI & Automation FAQ
We just want a chatbot on our documents.
That can be a real project. The prototype is quick. Making it accurate, honest about what it does not know, cheap to run and safe to expose is the actual work.
Which models do you use?
Whatever fits the task, the budget and your data-residency rules. We benchmark options against your evaluation set and design so you can switch.
How do you stop it from getting things wrong?
Grounding answers in retrieved data, strict prompt boundaries, input and output filtering, an evaluation set with adversarial cases, and deterministic fallbacks.
Do we need a data platform first?
Not always. If the data is reachable we can start on the feature and firm up the platform in parallel.
Have a ai & automation problem worth solving?
Tell us what you're working on. We come back within two business days with a point of view and next steps.