Services
How we work
We design and build AI systems with language models, computer vision, audio and classical machine learning, and integrate them into the infrastructure our clients already run. We work with SMEs and larger organizations.
We can come in at any of the stages below and hand over to your team at whatever point suits you: after an assessment, after a working prototype, or with a production system running.
Technical Feasibility Assessment
A 2–5 day assessment of one specific AI use case: whether it can be built, how, and with what effort.
- → Kick-off workshop to understand your idea and get an overview of your current systems and data
- → Data availability and quality evaluation
- → Architecture options suited to the problem (LLM, vision, audio, classical ML, multimodal)
- → Build-vs-buy analysis and compute requirements
- → Effort estimation for prototype or production
- → Deliverable: 5–20 page Technical Feasibility Report with go/no-go recommendation
AI Rapid Prototyping
A focused 3–8 day sprint that produces a working proof of concept on your real data, in language models, vision, audio or classical ML.
- → Kick-off workshop to capture requirements and success criteria, and to set up access to your data and environment where the prototype needs it
- → Prototype implementation on our side, using your real data
- → Evaluation against the agreed success criteria
- → Closing presentation and write-up of the technical findings, caveats and pitfalls, as the basis for deciding how to proceed
- → Deliverable: working prototype code (client-owned), evaluation metrics, and recommendation to proceed
AI Product & System Development
End-to-end development of production-ready AI systems, integrated into your infrastructure.
- → Kick-off workshop to agree on scope and architecture, and a midpoint review to check progress and that the integration into your systems is on track
- → Language models (RAG, agentic systems, fine-tuning), computer vision, audio and speech, and classical ML (forecasting, recommenders, anomaly detection)
- → Integration into your stack: REST APIs, your cloud provider's services, ERP connectivity
- → Deployment in your environment: your cloud account (Azure, AWS, GCP), on-premises (Docker, Kubernetes, vLLM, Triton), or at the edge for low latency
- → Deliverable: deployed system with source code (client-owned), documentation, and a handover session with your team so they understand the system
Ongoing Technical Advisory
Senior AI engineers on call, after the handover of a system we built or to complement your team's expertise.
- → Regular check-ins on your AI systems and what's coming next
- → Questions answered between check-ins
- → Design and code reviews of your team's work
- → Evaluation of new models, vendors and tools when they could change your setup
- → Days per month agreed up front and adjustable as your needs change
Get started
Have an AI problem worth solving?
Tell us what you're trying to build, or where an existing system is stuck. We'll tell you if we can help, and what it would take.
An idea, but no AI team
You can see where AI could help your business, but there is nobody in-house to talk it through with, let alone design or build it. We start with a conversation about the idea and take it through as many of the stages above as it needs.
A pilot that stalled
It worked in a demo but never reached production, or it never quite worked. We start by looking at what was built and why it stalled. Depending on what we find, the next step is a fix, a new approach, or a clear recommendation to stop.
Running costs that are too high
Your AI works, but paying for it per request adds up. We look at where the cost comes from and what could replace it: a smaller model, a self-hosted one, or fewer calls through a different design. Then we work out how to show that the results hold up, and take the change through the stages above like any other project.