One senior team for the full arc of an AI system — framing the problem, choosing the approach, building it properly and keeping it healthy in production.
Most AI projects don't fail on the model. They fail on problem framing, data, evaluation or the path to production. We treat those as the core of the work, not the edges of it.
Every engagement is led by our principal AI architect working alongside data scientists who write the code. We start by mapping where AI creates real leverage in your business, then design a system your team can understand, operate and extend — whether that is a tool-using agent, a forecasting model, a retrieval-augmented LLM application or a computer-vision pipeline.
We are deliberately technology-agnostic. We choose between hosted and open-weight models, classical ML and deep learning, building and buying, based on your constraints — accuracy, latency, cost, data residency and the skills of the team who will run it.
Multi-step agents that reason, plan and call tools across your stack — research copilots, operations automation and autonomous workflows. We design the orchestration, permissions and fallbacks so agents act safely, and we measure task success, not just response quality.
Classical and modern ML for forecasting, ranking, recommendation, anomaly detection and personalisation. We build the data and feature pipelines, train and tune models against honest baselines, and put the MLOps foundations in place to keep them healthy.
Retrieval-augmented generation, structured extraction, summarisation, classification and reasoning built on modern LLMs — grounded in your documents and data, and evaluated against the tasks that matter to you.
Vision, sequence and multimodal models, from research prototype to hardened, observable service. We work across PyTorch, JAX and modern serving stacks, and optimise for the latency and hardware you actually have.
Work directly with our principal AI architect to frame the right problem, choose the right approach and design a system your team can operate — before significant budget is committed.
We map the problem, the data and the constraints, and agree measurable success criteria — including when AI is not the right answer.
Architecture, model choices, evaluation plan, guardrails and cost envelope, agreed with your team before the build starts.
Short iterations with working software at each step: production code, pipelines, observability and continuous evaluation.
Monitoring, retraining and human-in-the-loop review — then a clean handover, or ongoing support if you prefer.
Not always. Many LLM and agentic systems work with the documents and systems you already have. For custom ML we assess data volume and quality during discovery, and tell you plainly if it isn't enough yet — and what it would take.
Whatever fits your constraints. We work with hosted and open-weight models, the major cloud platforms and on-premise deployments, and choose on accuracy, latency, cost and data residency rather than preference.
We work inside your environment wherever possible, follow least-privilege access, and agree data-handling rules — including what may and may not be sent to third-party model providers — before any work starts.
Yes, and we prefer it. We build in your repositories and pair with your engineers throughout, so the capability stays in-house after we step back.
Already have AI in production? We audit, tune and stabilise — improving quality, cost and latency, and building the evaluation discipline your team needs to keep it that way.
View serviceAI-assisted golden image creation, fleet deployment and OS hardening — build once, lock it down, and keep every endpoint drift-free against a hardened baseline.
View serviceAI-augmented offensive security testing paired with senior human validation — continuous attack-surface discovery, not a once-a-year PDF.
View service