Unified capabilities.
One accountable team.
Our pods carry AI engineering, data engineering, application development and governance under one delivery owner — so nothing falls through the seams.
Strategy & governance
Decide what to build, and prove it is defensible.
AI engineering
Generative, agentic, vision and predictive systems in production.
Data & platform
The pipelines, platforms and MLOps that make AI reliable.
Build & run
Full-stack applications and managed operations after go-live.
Strategy & governance
Decide what to build, and prove it is defensible.
AI strategy & advisory
2–6 WEEKSDecide what to build before you spend a year building it.
Scored use-case portfolio, prioritised roadmap with effort and cost ranges, data readiness report, financial model.
AI governance, risk & compliance
6–12 WEEKSMake your AI defensible before a regulator, an auditor or a board asks you to.
Governance framework, AI system register with risk tiers, testing methodology and results, control catalogue, audit evidence pack.
Enablement, training & CoE-as-a-Service
RETAINERBuild the internal capability, so you are not dependent on us forever.
CoE charter, standards library, trained internal team, reusable engineering assets.
AI engineering
Generative, agentic, vision and predictive systems in production.
Generative AI & LLM engineering
8–16 WEEKSBeyond the chatbot. Generative systems that are grounded, evaluated and safe to put in front of a customer.
Deployed system, evaluation report against agreed criteria, guardrail configuration, model card, runbook.
Agentic AI & intelligent automation
18–26 WEEKSSystems that do the work, not just describe it — with a human in the loop where it matters.
Production agent workflow, approval and escalation design, trace logging, cost model.
Machine learning & predictive analytics
6–14 WEEKSThe unfashionable models that still generate most of the measurable value.
Deployed models, evaluation and monitoring plan, retraining pipeline, business impact measurement.
Computer vision
20–30 WEEKS INCL. DATA-COLLECTION PHASETurn cameras you already own into a source of operational data.
Trained vision models, edge or cloud deployment, labelling pipeline, accuracy report.
Conversational AI & voice
6–14 WEEKSAssistants that resolve, escalate cleanly, and know what they do not know.
Deployed assistant, integration layer, escalation design, analytics dashboard.
Data & platform
The pipelines, platforms and MLOps that make AI reliable.
Data engineering & lakehouse build
8–20 WEEKSBuild the foundation that makes all your AI work.
Ingestion pipelines, data quality framework, catalogued lakehouse, governance controls.
MLOps & model operations
6–12 WEEKSModels that stay accurate in production — not just on the day they launched.
CI/CD for models, drift monitoring, retraining triggers, cost and performance dashboards.
Cloud AI infrastructure
4–10 WEEKSRight-sized, secure, observable infrastructure for AI workloads.
Infrastructure-as-code, security posture review, cost optimisation report, runbooks.
Build & run
Full-stack applications and managed operations after go-live.
Managed AI operations
RETAINERWe run your AI systems post-launch so your team can focus on what's next.
SLA-backed operations, incident response, regular model reviews, usage and cost reporting.
Tell us the process. We will tell you whether AI helps.
A thirty-minute conversation with a solution architect — not a sales call. Bring one process you think is a candidate. You will leave with a straight answer.