Our AI Lab builds intelligent systems that perceive, reason and act in the physical world: for medical devices, electronics manufacturing and industrial automation. We concentrate on four technology directions rather than spreading across every AI application.
Innovobot’s Artificial Intelligence Lab engineers Physical AI systems that perceive, reason and act reliably in complex, real-world environments.
We turn promising AI models into deployable products by integrating perception, reasoning and action into existing equipment, engineered to operate at line speed, withstand harsh environments and satisfy the reliability demands of regulated workflows.
Four technology directions
Each direction was chosen for the same reason: today’s tools don’t go far enough, and the gap is worth engineering into.
Combine multiple cutting-edge A.I. techniques to satisfy computer vision use cases that are not yet addressed commercially because of their challenging characteristics:
Outperform first-generation AI MedTech by putting the latest AI models into medical devices.
Mapping regulatory constraints to optimize the integration of cutting-edge A.I. into medical devices as extensively as possible.
Explainability for FDA review · reliability · complex decision support
Build agentic reasoning methods that generate genuinely new solutions at a fraction of the usual cost:
New ways of combining frontier models against niche problems, across engineering roles.
Frontier models combined with proven frameworks and domain expertise.
Solve one narrow problem end to end, and use it as the proof that unlocks broader dexterous robotics:
Building a demonstrated dataset for dexterous robotics.
What the directions look like in practice.
Partner: Waste Robotics
The challenge
Waste Robotics already had deep in-house computer-vision expertise, but real-world variation was limiting performance across deployments and annotation effort slowed every improvement cycle.
Our approach
Our specialists embedded alongside their team for three months, benchmarking and rebuilding the vision, data and annotation workflows, and strengthening reproducibility, inference efficiency and field adaptation.
Results
A significantly improved core computer-vision component, reaching strong performance with less data and faster execution.
Partner: health-support platform
The challenge
The concept needed more interactivity, adaptability and resilience, voice, memory and trusted knowledge had to operate as one system, and sensitive use required privacy, traceability and hallucination controls.
Our approach
We designed the speech, language, data and orchestration layers, structuring specialized agents, grounded retrieval and persistent memory, with guardrails, monitoring and modular foundations specified.
Results
A target architecture tied to real product and user needs, and a defined path from MVP to a scalable multi-agent platform.
Partner: Oneka
The challenge
Remote ocean assets operate under harsh, high-motion conditions where mechanical faults and sensor drift create unplanned downtime, and large, diverse data streams were monitored without embedded AI.
Our approach
Sensor fusion and edge AI designed for the data-acquisition platform, with anomaly detection and system-health models, mapping operational data into maintenance and optimization insights.
Results
A clear implementation path for real-time asset-health monitoring, a foundation for earlier fault detection, and a reusable concept for other harsh-environment assets.
Partner: MileBox
The challenge
Urban delivery must balance speed, bike paths, battery range and traffic, operators lacked a unified view of bike health and delivery performance, and unexpected failures increase downtime and cost.
Our approach
We defined the sensor, fleet-management and control-centre capabilities, developed dynamic routing around real bike infrastructure, and added battery intelligence, failure alerts and predictive maintenance.
Results
A clear path from connected-bike data to AI-driven operations, and a roadmap for route efficiency, bike readiness and maintenance planning.
Applied AI, deployed responsibly and at scale
AI designed alongside our Robotics, HMI and IoT Labs, so perception, control and mechanics are one system rather than three suppliers.
Models built for regulated and safety-critical environments: explainability, edge deployment and validation evidence.
Co-developed IP structured for shared ownership and revenue, including non-competing applications in other sectors.
Bring us the problem that has to work in the real world.
Tell us what’s breaking. We’ll tell you honestly whether AI is the fix and which of our Labs it needs.
