We design, build, pilot, and hand over connected products that work past the demo — embedded engineering, intelligent sensing, cloud, and governance delivered by one team, so your system ships secure, measurable, and field-ready.
We do not split hardware, software, data, and governance across disconnected vendors. That reduces handoff risk, shortens decision cycles, and produces coherent systems — rather than stitched-together pilots that stall before deployment.
We design and deploy machine learning models that run on constrained hardware — edge inference, anomaly detection, drift monitoring, and on-device decision logic. Models are scoped to your memory budget, power envelope, and latency requirements, then validated against real sensor data before deployment.
Explore servicesEnd-to-end device engineering from sensor selection through firmware, connectivity architecture, cloud ingestion, and fleet management. Every design decision accounts for deployment realities — intermittent connectivity, hardware variation, OTA update risk, and operational lifespan.
Explore servicesAI inference on microcontrollers and resource-limited processors. We handle model compression, memory profiling, RTOS integration, and energy benchmarking so the intelligence in your device survives real field conditions — not just lab benchmarks.
Explore servicesFrom hardware specification through firmware, telemetry pipeline, observability dashboard, and governance documentation — one team, one sprint, one coherent handover. We treat deployment runbooks and capability transfer as first-class deliverables, not afterthoughts.
Explore servicesOur projects are scoped around deployment realities: power budgets, network instability, data integrity, observability, security, and operational handover. Every engagement is judged by measurable outcomes — not presentation polish.
Climate-adaptive precision farming for smallholder farmers. AgroSense AI combines solar-powered soil sensors, LoRaWAN connectivity, satellite intelligence, AI advisory, and SMS-first delivery to help farmers make better daily decisions under climate uncertainty.
Low-power sensor nodes ingesting air quality, temperature, and humidity data to a cloud dashboard with real-time anomaly alerts driven by edge AI. Designed for urban deployment with intermittent connectivity and low maintenance access.
Vibration and thermal sensors on manufacturing equipment with an on-device anomaly detection model. The system identified bearing degradation patterns ahead of failure, enabling planned intervention and eliminating unplanned downtime across the pilot site.
GPS-enabled temperature tracking with on-device compliance logic for pharmaceutical cold chain. Automatic excursion alerts and tamper-resistant audit logs, structured to meet regulatory reporting requirements without manual collation.
Our ADEPT loop grounds every sprint in three proven delivery frameworks — Double Diamond, Google Design Sprint, and Scrum — adapted for the specific demands of AI and IoT product development. Each stage produces concrete outputs, not slide decks.
Define the deployment context, user outcome, system boundaries, and success criteria before any build decisions are made. We surface power budgets, connectivity constraints, compliance obligations, and integration points — so no assumption is carried forward unchecked.
Rapid architecture exploration using sprint logic: understand, sketch, decide, prototype, validate. High-risk decisions are stress-tested early — against real hardware, real data, and real operating conditions — before engineering investment is committed.
Build the device, firmware, data pipeline, and intelligence layer in iterative cycles. Security provisions, observability hooks, and governance controls are integrated from the first sprint — not retrofitted at delivery.
Deploy a controlled fleet under real operating conditions. Measure uptime, data quality, model performance, and security posture against the success criteria defined in Align. The pilot is a rigorous field experiment — not a demo.
Deliver system documentation, operational runbooks, maintenance guides, and training to your team. The goal is full operational independence — your organisation leaves the engagement capable of running, updating, and extending the system without us.
Connected products fail when engineering disciplines are separated. We keep system design, model behaviour, field constraints, and operational governance in one loop — from discovery through pilot and transfer. That coherence is what makes the difference between a prototype and a deployed system.
Device architecture, edge intelligence, telemetry pipelines, and governance are designed together by one team. Decisions that require trade-offs across those layers are resolved in the same sprint — not across vendor boundaries weeks apart.
When intelligent systems interact with the physical world, security and responsible AI controls are not optional extras. We implement device security baseline provisions and AI risk management from the first architecture session.
Our team has direct experience designing and operating systems in environments with intermittent power, limited connectivity, and constrained hardware. We do not assume ideal conditions — we engineer for the conditions that actually exist.
Every engagement produces documentation, runbooks, and training that allows your team to operate and maintain the system independently. We do not design for ongoing dependency — we design for your operational continuity.
If you have a device concept, a constrained environment, or an underperforming pilot, we will map the shortest path to a trustworthy working system — before any commitment is made.
No obligation · we respond within one business day with a recommended next step