Services

• AI Strategy

• Machine Learning

• Edge Computing

• Systems Architecture

The Challenge

The engineering team was talented and moving fast. What they needed was an architecture that could handle growing sensor data while keeping every machine safe, predictable, and responsive.

Perception, planning, and control had grown as separate systems over several years. Data moved between them through ad-hoc interfaces, adding delays that limited performance and made every new feature a risk to the stability of the whole fleet.

The team needed clear answers to key questions:

1. Where should decisions happen?
   Choosing between onboard and cloud processing affected speed and cost.

2. How do we reduce response time?
   Machines needed to react faster to people and obstacles around them.

3. How do models improve safely?
   New AI models had to be tested before reaching machines in the field.

4. Can the stack support new products?
   The architecture had to serve future robot types, not just today's fleet.

The Approach

AXIOMET combined AI strategy, systems engineering, and rigorous testing to create an intelligence layer the team could build on with confidence for years to come.

Discover

We analyzed the full software stack, from sensor input to motor control, measuring where time was lost, which components were fragile, and how data and models flowed between machines and the cloud.

Define

We set clear performance and safety targets with the team, then ranked the improvements that would deliver the fastest gains in response time while keeping operational risk as low as possible.

Architect

We designed a modular edge AI architecture with clean interfaces between perception, planning, and control, plus a cloud pipeline for training, simulation, and safe over-the-air model updates.

Validate

Every change was proven in simulation and on a small test fleet before wider release, giving the team hard evidence on latency, reliability, and safety before deploying to customer sites.

The Solution

Rather than optimising individual algorithms, AXIOMET rebuilt how intelligence flows through each machine and across the fleet. Faster onboard processing, clear system boundaries, and a safe update pipeline now allow the team to ship new capabilities without compromising reliability.

An intelligence layer built for autonomy at scale.

Machines now respond faster and more consistently, while a modular architecture lets the team add new sensors, models, and robot types without rebuilding the core system.

Solution

• Edge AI Architecture

• Real-Time Perception Pipeline

• Simulation & Testing Framework

• Over-the-Air Model Updates
  
  

“AXIOMET gave our machines a faster, more reliable brain and gave our engineers a platform they can keep building on with confidence.”