Edge AI Retrofit for Existing Hardware

What is an edge AI retrofit?

An edge AI retrofit is the engineering work that turns an existing piece of equipment into an intelligent one, with the inference running on or beside the device rather than in a remote cloud. The installed system keeps its job. The retrofit adds sensing where needed, a right-sized compute module, firmware that integrates with the existing control logic, a model validated on that hardware, and the security controls a connected device needs.

It differs from a software-only AI project in one important way: the hardware is a fixed constraint. Power, interfaces, enclosure heat, memory and service life are decided by what is already in the field, so the retrofit is engineered around them rather than the other way round.

Why retrofit rather than replace?

Most operational equipment is mechanically and electrically sound long after its control electronics stop being modern. Replacement buys new capability at the cost of downtime, re-certification, retraining and the write-off of assets that still work. A retrofit preserves the installed base and adds only what the new capability needs.

• Lower capital outlay, because the mechanical and electrical plant stays in service.
• Shorter change windows: the retrofit is designed to install alongside running equipment.
• Fewer unknowns: interfaces, wiring and workflows are already understood on site.
• A defined path to scale once the first units prove the value on target hardware.

Replacement is still the right answer when the existing system cannot host the workload at all. That is the first thing a retrofit assessment establishes, and we say so when it applies. The retrofit-versus-replacement economics are set out on the home page.

Systems that can be upgraded

• Analogue and IP CCTV cameras gaining on-device detection and event intelligence.
• PLC-controlled production lines gaining vibration, current and thermal sensing with local anomaly detection.
• Field machinery and agricultural equipment gaining perception and precision control.
• Sensor networks and gateways gaining local analytics and condition monitoring.
• Controllers and machines that need secure connectivity, telemetry and remote update.

Whether a specific system qualifies depends on its interfaces, spare power and the latency the application tolerates. Our representative applications are described on the Technology page and are labelled by maturity.

Hardware interface assessment

Every retrofit starts by establishing what the installed system already provides: available interfaces (analogue video, Ethernet, RS-485, CAN, Modbus, digital I/O), spare power and mounting space, existing control logic, documentation, and the condition of the fleet. The assessment produces a documented baseline and a feasibility judgement before any hardware is chosen.

Where documentation is missing or hardware revisions differ across a fleet, the assessment includes bench measurement of the actual signals.

Edge compute options

The compute module is sized to the workload, not to a preferred vendor. A microcontroller with a small model may be enough for a threshold or anomaly task; an NPU or GPU module suits vision workloads; an FPGA suits fixed, deterministic pipelines with hard latency limits. The trade-off is always between capability, power, heat and unit cost at fleet volume. Compute selection and board development are covered under AI hardware engineering.

Embedded firmware integration

Firmware is where the retrofit meets the existing control logic. Board bring-up, drivers for the added sensors, an Embedded Linux or RTOS platform, watchdog and recovery behaviour, and a signed update path are engineered so the retrofit can be maintained for the life of the fleet. Details are under embedded firmware and edge platforms.

AI deployment and optimisation

Models are selected, compressed and quantised for the chosen target and validated on that hardware with representative data. Accuracy is reported alongside false-positive and false-negative rates, latency and power draw, because a model that meets its accuracy target but exceeds the thermal budget is not deployable. See embedded AI engineering.

Cybersecurity

A retrofit turns an isolated device into a connected one. Threat modelling, a hardware root of trust where the platform supports it, device identity, signed updates, runtime hardening and monitoring are designed in from the first architecture decision. The nine controls we apply are described under embedded and edge cybersecurity.

Performance, power and thermal constraints

Six numbers decide a retrofit design: latency, power, memory, thermal headroom, lifecycle and unit cost. Each is set as a target, measured on the real hardware in the real enclosure, and traded against the others explicitly. A retrofit may require additional compute where the existing processor cannot satisfy the latency, memory or thermal requirement; the assessment says which case applies.

Validation methodology

We validate where the system must operate, not only in a development environment. Every published result carries its target hardware, firmware and model versions, operating conditions, dataset and sample size, method, tolerances, known limitations and test date. Until target testing is complete we publish no illustrative performance numbers.

Deployment lifecycle

A retrofit is not finished at first power-on. Rollout, telemetry, update orchestration, recovery to a last-known-good state, component lifecycle tracking and security review are planned for the years the fleet will run. The Technology page shows how the six layers of the stack are engineered to work as one.

Typical project stages

1. Assess: interface and constraint baseline, feasibility judgement, retrofit-versus-replacement recommendation.
2. Engineer: compute selection, hardware, firmware, model and security architecture proven on a prototype unit.
3. Validate and scale: evidence on the target system, a pilot on representative units, then fleet rollout with update and recovery in place.

Each stage has a defined output and a decision point, so a programme continues on evidence rather than momentum.

Related capabilities

Embedded AI engineering · AI hardware and electronics R&D · Embedded firmware and edge platforms · Embedded and edge cybersecurity · High-speed PCB and product engineering · All capabilities · How the edge stack works