Services
Everything here starts from the same place: a decision about what to build. Consulting answers that question; the rest of this list is what happens after it is answered. Some clients only ever buy the first thing. If you are not sure which applies, describe the problem and we will tell you which we would propose and why — including when the answer is that you do not need us.
Consulting
The advisory work, and where most engagements begin. Architecture and system design, technical due diligence, build-versus-buy, roadmap sequencing, and whether a problem is an AI problem at all.
- IT & AI consulting — independent advice on what to build and how it should be shaped, with a plan you own and can take anywhere.
AI systems
Three purchases that get conflated because they all involve a model. Each fails in a different way, so each has its own page.
- AI implementation — taking a model from a convincing demo to a system with evaluation, cost control, and a rollback path.
- LLM integration and RAG — connecting a model to your own data without it inventing the parts it does not know.
- AI agents — multi-step workflows that act rather than answer, with the guardrails that makes tolerable.
Engineering services
Scoped around a technical outcome rather than a team shape, and usually delivered by one of the team shapes below.
- Software engineering — web, mobile, and backend systems built to your business rules.
- System integration — connecting ERP, CRM, payment, and third-party systems into one reliable flow.
- DevOps & cloud — CI/CD, infrastructure as code, observability, and cloud cost work.
Delivery teams
The engineers who build what the consulting recommended. The two options differ in who is accountable when a date slips.
- Delivery teams — managed delivery where we own the outcome, or a dedicated team working inside your process.
- IT staff augmentation — named individuals filling specific gaps in an existing team, managed by you.
AI training data
The data an AI system learns from, collected and labelled by the team that then has to train on it. The overview page explains how the four fit together.
- Data collection — video, image, audio, document, and scripted scenarios captured to a written specification.
- Egocentric and exocentric capture — synchronised first-person and multi-view recording for robotics and embodied AI.
- Data annotation — image, video, 3D point cloud, document, and audio labelling.
- RLHF and SFT data — demonstrations, preference ranking, evaluation, and red teaming for LLM post-training.
How to choose
If you do not yet know what should be built, start with consulting — everything downstream is cheaper once that is settled. If you do know, the practical test is who will be accountable when a deadline slips. If that has to be us, you want managed delivery. If your own engineering manager will own it, a dedicated team or staff augmentation fits better and usually costs less.
Frequently asked questions
- Do we have to start with consulting?
- No. If the decision is already made and you need it built, we can start at delivery. We would rather spend a short paid discovery first, because building the wrong thing well is the most expensive outcome available to either of us.
- Can we change engagement shape partway through?
- Yes, and it is common. Engagements often start as a scoped project and move to a dedicated team once the direction is clear. Changing shape is a contract amendment, not a restart.
- Do you take on work on an existing codebase?
- Yes. Most of our work is on systems that already exist and already have users. We start with a short read-through of the code and infrastructure before quoting anything.
- What is the smallest engagement you take?
- Small enough to be a real test of whether we work well together. Tell us the first slice you would want delivered and we will say whether it is a sensible starting point.
Tell us what you are building
Send a short description of the system and the constraint you are hitting. We reply within one business day.
Book a consultation
