Industrial edge intelligence for manufacturers

Making unplanned downtime, equipment failure, asset inefficiency, excess maintenance, blind spots, manual readings, clipboard rounds, guesswork and data gaps a thing of the past.

Get the operational data you’re missing, without a major IT project. Practical industrial sensing, resilient site connectivity and local dashboards show what is happening across your equipment and environment. Start with visibility today and build the data foundation for predictive maintenance tomorrow.

Discuss your monitoring opportunity See how the platform works

Illustrative scene: an engineer with a tablet beside a motor-driven pump. Sensors on the motor and pump bearing send data to an on-site edge node on a shelf, and a wall screen shows an ATF local dashboard with vibration, bearing temperature and motor current. — Illustrative scene: sensors, edge node and local dashboard

Edge-first: Data is processed and stored on site by default.
Low disruption: Designed to sit alongside existing IT and OT systems.
Human-centred: Insight that supports engineering judgement.

Industrial AI starts with usable data.

Many manufacturers know where they want to get to: fewer unexpected failures, better maintenance decisions and more efficient operations. The challenge is obtaining reliable, contextual data without complex integration work, major infrastructure changes or surrendering control of sensitive operational information.

Illustrative scene: a plant room with pumps, a process vessel and air handling units. Callouts show where ambient, pump-motor, vessel and air-handling measurements could be taken, all linked to an on-site edge node. — Illustrative scene: typical monitoring points in a plant room

Limited visibility

Critical equipment and environmental conditions are not continuously visible.

High friction

Traditional monitoring projects can require cabling, integration and specialist support.

Uncertain path to AI

Without a trusted data foundation, predictive maintenance remains out of reach.

A practical first step towards predictive maintenance.

Capture

Low-power sensing and industrial connectivity for environmental and operational telemetry, including temperature, humidity, air quality, vibration, motor current and other applicable inputs.

Understand

Local dashboards, trends and threshold alerts that help teams see conditions, investigate change and make better operational decisions.

Build towards prediction

A demonstrator linked to a training rig shows how condition data and machine behaviour can support anomaly detection and future predictive-maintenance workflows.

Available now

Sensing, connectivity, local monitoring, dashboards and threshold alerts.

In development and validation

Advanced anomaly detection and predictive insight.

Demonstrated in an industrial setting.

Drawing-style plan of one facility with a structural grid, rooms, seven environmental sensors labelled S1 to S7 and an on-site edge node. Representative layout, not to scale.

Environmental monitoring
7 locations
1 facility
Single-day install

Environmental monitoring deployment

At a UK government-backed manufacturing centre of excellence, ATF deployed environmental sensors across seven locations in one facility in a single day. The deployment provided real-time dashboard visibility of air-quality and environmental conditions, demonstrating edge-based monitoring in a working industrial setting.

This was a single-site demonstration deployment focused on environmental monitoring.

Drawing-style elevation of the training rig: a motor driving a shaft on two bearings to a load, with vibration, temperature and current sensors listed in a schedule and linked to an edge node and dashboard.

Condition monitoring
Data capture
Visualisation

Training-rig demonstrator

Our demonstrator model is linked to a training rig, showing how equipment-condition data can be captured, visualised and used to explore the next step towards predictive maintenance.

A demonstrator for condition monitoring and the route towards anomaly detection; not a customer implementation.

Data stays under your control. Insight happens where the work does.

Illustrative scene: close-up of an accelerometer on a motor bearing housing, with a callout showing a high-rate vibration signal being processed at the source. — Illustrative scene: processing at the sensor

Local processing

Collect, store and analyse data close to the asset.

Designed for industrial environments

High-rate signals are reduced to useful features at the sensor, so connectivity can be matched to the site, including long-range, low-power wireless where cabling is difficult.

Light on integration

Runs on its own sensors and edge node, with minimal dependency on existing IT and OT systems.

Customer-controlled connectivity

Cloud sync, remote support and off-site analysis are optional and subject to customer approval. What leaves the site, if anything, is your decision.

We design ATF to support human judgement, not replace it. Alerts should come with the context and reasoning behind them, so your team can check them and decide.

Read our vision for edge intelligence

An edge architecture built for the plant floor.

Each layer does its work as close to the asset as possible, so only the information people need to act has to travel.

Isometric illustration: sensors on a motor-driven machine send processed data along dashed routes to an on-site edge node, which serves a local dashboard. A connection to the cloud is optional.

01 Sense
Industrial sensors
Vibration, temperature, motor current and environmental conditions, placed where they inform the components that matter.
02 Process at source
Feature extraction
High-frequency signals are processed at or near the sensor, so only meaningful features need to travel across the site.
03 Connect
Site-appropriate link
Connectivity matched to the environment: existing networks where available, long-range, low-power wireless where cabling is impractical.
04 Edge node
Store, analyse, alert
An on-site edge node holds the data, runs the analytics and serves local dashboards and threshold alerts, and is designed to keep working without an internet connection.
05 Optional
Customer-approved cloud
Cloud sync, remote support and off-site analysis only when you choose to enable them.

From raw signal to useful insight

In development and validation

Concept illustration in four stacked panels: a raw high-rate vibration signal, its frequency spectrum processed at the sensor, compact features sent to the edge, and an edge-node chart showing a signal moving outside its learned normal band. — Concept illustration: values are illustrative

High-rate vibration is reduced to compact features at the sensor. The edge node is designed to compare them with each machine’s learned normal behaviour, so meaningful change can be seen and raw data can stay on site.

01Raw signal at the sensor
02Spectrum, processed at source
03Compact features sent to the edge
04Compared with learned normal

How our analytics are designed

Start with a focused, useful deployment.

Identify a monitoring opportunity
Choose an asset, production area or environmental concern where better visibility would create value.
Deploy and learn
Install sensors and edge infrastructure with minimal disruption, then establish a baseline of normal conditions.
Build confidence and capability
Use the data to improve visibility and decision-making, then prioritise the next use case for alerts, condition monitoring or predictive insight.

Engineers, operators and growth leaders.

Our team pairs manufacturing and engineering experience with start-up and scale-up track records. We understand the shop floor as well as the boardroom.

David Poole

Chairman

25+ years in tech-enabled services, with deep AI and automation expertise and a track record of investing in and building businesses from start-up to exit.

Charles Hoult

Chief Executive Officer

Chartered engineer and former MOD senior leader, experienced in delivering complex, high-growth programmes in demanding environments.

Andre Le-Lerre

Chief Architect

Seasoned management consultant with deep expertise in large-scale digital transformation; past roles span Ericsson, EY and the European Space Agency.

Jon Hunt

Chief Operating Officer

Two decades guiding businesses such as Biomni, Unisys and DXC through SaaS, automation and AI; an expert in strategic IT solutions.

Iain Crosley

Commercial Director

Experienced industrial-engineering executive; professor at the University of Central Lancashire and Advisory Board member of the AMRC.

Tas Papasolomontos

Chief Financial Officer

Senior business advisor and finance leader for complex, high-growth businesses; most recently CFO of a multinational manufacturer in major transformation.

Start with visibility. Build towards resilience.

Tell us about the equipment, area or environmental concern you would like to understand better, and we will discuss whether an edge-based monitoring deployment is a good fit.

Discuss your monitoring opportunity Take the 3-minute fit check

Or email hello@aitechfutures.com directly, including if you would like a technical overview.

Making unplanned downtime, equipment failure, asset inefficiency, excess maintenance, blind spots, manual readings, clipboard rounds, guesswork and data gaps a thing of the past.Making unplanned downtime a thing of the past.