Sheffy Adey is an engineering-led AI + IoT lab focused on one outcome: connected systems that leave prototype mode and keep working under real-world constraints. We do not optimise for demos. We engineer for deployment.
To help organisations move from concept to dependable deployment by combining hardware, embedded systems, data, intelligence, and governance in one execution model — not a chain of disconnected specialists.
We design for unstable connectivity, power limitations, environmental conditions, maintainability, and risk control from the start. That makes our systems more deployment-ready and easier for client teams to inherit — without rework at the handover stage.
A useful intelligent system is not only accurate. It is observable, explainable enough for its operational context, secure by design, and operable by the team that owns it after handover.
Accuracy metrics measured in controlled conditions are necessary — but they are not sufficient evidence that a system will hold up under the conditions it will actually face: intermittent connectivity, hardware variance, data drift, and operators who were not involved in the build.
We design for those conditions from the architecture stage. That means the gap between pilot and production is smaller — and the documentation exists to close it.
Most teams can demonstrate something working in controlled conditions. Reliably shipping a system that holds up under hardware variation, data drift, and real operational load is a different problem. That is the one we are set up to solve.
Organisations evaluating us for funded programmes, procurement frameworks, or technology partnerships need more than a capabilities overview. Here is what our engagements actually produce.
Architecture choices, component trade-offs, risk assumptions, and configuration decisions are written into deliverable documents — not held in the heads of the people who built the system. Outputs are structured to support audit, review, and continuity.
Power budgets, connectivity drop-out, sensor degradation, and limited operator bandwidth are treated as requirements — not exceptions. Systems are stress-tested against realistic operating conditions before they are considered pilot-ready.
Every engagement produces a handover pack: runbook, maintenance guide, security checklist, and training session. The test of a successful engagement is whether the client team can operate the system without us — and we design every deliverable toward that outcome.
Monitoring dashboards, drift alerts, and performance reports are scoped into the work — not treated as optional add-ons. Clients receive defined metrics aligned to operational targets, not lab benchmarks that do not translate to the field.
Security controls, access policies, data handling procedures, and risk registers are produced during build — not after. Deliverables reference NISTIR 8259, ETSI EN 303 645, and NIST AI RMF where applicable, and are structured for regulatory or funder review.
Every engagement begins with a written scope, a defined deliverable set, and agreed acceptance criteria. No open-ended retainers. No ambiguous milestones. Scope is confirmed before any build work begins, which gives procurement and programme teams a clear basis for approval.
Our work references established technical and governance standards. Deliverables are structured so they can be mapped to grant milestones, TRL assessments, or procurement due-diligence checklists.
Device work references NISTIR 8259 for IoT device cybersecurity capability baseline requirements and ETSI EN 303 645 for consumer IoT security. Security controls are documented with the rationale for inclusion or explicit exclusion.
Intelligence system design references the NIST AI Risk Management Framework. Model decisions, confidence thresholds, fallback logic, and monitoring plans are documented in a format accessible to non-technical governance reviewers.
Architecture briefs, risk registers, and handover packs are structured to support Innovate UK, UKRI, and equivalent programme milestone reporting — including Technology Readiness Level documentation and project deliverable records.
Data pipeline design includes a classification step, retention policy, and processing record. For systems handling personal or operationally sensitive data, we apply privacy-by-design principles with documented controls aligned to ISO 27001.
Let's review your constraints, operating environment, and target outcome — then define the right sprint, deliverable set, and acceptance criteria before any build work begins.
First call is 45 minutes. We will confirm fit before any scope is proposed.