We help teams design, build, pilot, and operationalize AI + IoT systems across the device, data, intelligence, and governance stack. Every engagement ends with something the client can run, audit, and extend.
Each service area produces specific, documented deliverables. Work can be engaged individually or as a full-stack programme — both paths are designed to leave client teams able to operate without ongoing dependency.
Sensor selection, electronics architecture support, MCU/SoC firmware, telemetry protocols, OTA update strategy, and reliability planning for constrained environments — built around operational requirements, not a generic stack.
Data pipeline design, edge or cloud inference architecture, model compression, thresholding, drift monitoring, and performance instrumentation — aligned to operational goals, not benchmark scores.
Secure telemetry ingestion, operational dashboards, alert logic, APIs, and access-aware integration — giving teams visibility into device health, fleet status, and system behavior at scale.
Runbooks, deployment checklists, risk controls, maintenance guidance, access policies, training, and documentation — so client teams can own, audit, and extend the system without continued dependency on us.
Every engagement starts with a written scope, a defined deliverable set, and agreed acceptance criteria. No open-ended retainers. No ambiguous outputs.
A structured technical review that produces an executable architecture brief, a component recommendation, and a clear risk register. Designed to remove ambiguity before build decisions are made — useful for grant applications, board review, or procurement scoping.
An end-to-end build sprint producing a pilot-capable system with working device firmware, a cloud pipeline, a monitoring dashboard, and an initial handover pack. Performance benchmarked against defined operational targets. Acceptance tested before handover.
A structured diagnostic and remediation engagement for systems that have stalled in pilot. We assess the architecture, identify root causes — whether data quality, model performance, integration gaps, or governance — and produce a recovery plan with prioritised actions.
Structured knowledge transfer, documentation review, and embedded coaching for internal engineering or operations teams. Covers system architecture, operational procedures, monitoring interpretation, and governance. Ends when the internal team can operate and extend the system without external support.
Deliverables are structured to support grant reporting, procurement evaluation, and internal governance review. We reference established standards and document decisions so outputs are traceable and defensible.
Device and cloud work references NISTIR 8259 for IoT device cybersecurity baselines and ETSI EN 303 645 for consumer IoT. Security controls are documented with rationale, not just implemented.
Intelligence system design references the NIST AI Risk Management Framework. Model decisions, thresholds, fallback logic, and monitoring are documented in a format suitable for review by non-technical stakeholders.
Architecture briefs, risk registers, and handover packs are structured to support procurement due diligence and grant milestone reporting — including Innovate UK, UKRI, Horizon-adjacent programmes, and technology readiness assessments.
Data pipeline design includes a data classification step, retention policy, and processing record. For systems involving personal or sensitive data, we work to privacy-by-design principles with documented controls.
We will recommend the shortest execution path and the deliverables needed to de-risk it — whether that is a single architecture brief or a full pilot sprint.