Services

From feasibility to production

Four ways we work. Most engagements start with a scoped feasibility read and end with a system your team can own.

01

Computer-vision systems

Detection, tracking, and 3D understanding tuned to your optics, lighting, and latency budget, and proven on your own line.

Capabilities

  • Defect detection, classification, and segmentation
  • Measurement, alignment, and 6-DoF pose estimation
  • Multi-camera, stereo, and depth pipelines
  • Lighting and optics specification
  • Dataset design, labelling, and synthetic augmentation

Deliverables

  • A calibrated, documented inference pipeline
  • An evaluation report measured on your data
02

Robotics & embodied AI

Perception-to-action stacks for manipulation, navigation, and collaborative workcells, with state estimation you can reason about.

Capabilities

  • Localisation and sensor fusion (LiDAR, stereo, IMU)
  • Motion and grasp planning
  • Obstacle handling in mixed human-robot spaces
  • ROS 2 integration and behaviour design
  • Simulation and closed-loop evaluation

Deliverables

  • A working perception-and-control stack on your hardware
  • Defined fallback and safe-stop behaviour
03

Edge deployment & MLOps

Packaging, telemetry, and regression hooks from Jetson to industrial PCs, so your team trusts what the fleet reports once it is running on real sites.

Capabilities

  • Model optimisation and quantisation (TensorRT, ONNX)
  • Packaging for edge and industrial targets
  • Observability, logging, and drift monitoring
  • Regression tests and data-feedback loops
  • Over-the-air update and rollback paths

Deliverables

  • A reproducible build that runs unattended on target
  • Monitoring and a regression suite your team can extend
04

Perception audits & prototyping

An honest read on whether an idea is buildable inside your constraints, before anyone commits to a full programme.

Capabilities

  • Feasibility studies against real data and hardware
  • Optics, sensor, and compute selection
  • Rapid prototypes on representative samples
  • Risk, cost, and timing assessment
  • Review of an existing pipeline

Deliverables

  • A written feasibility report with measured numbers
  • A rough prototype and a recommended path

Bring us the hard part

Tell us what the machine needs to see.

Share your environment, sensors, and what “good” looks like. We’ll tell you what is buildable, what is not, and where we would start.

[email protected]