Deployment offerings

Perception for Deployment

Tilius develops modular and custom embedded perception systems for teams that need robust AI performance outside the lab, on hardware that must operate within real power, latency, memory, and integration limits.

01 Edge vision

02 Sensor fusion

Deployment Core Runtime + accelerator + sensor I/O

03 Model optimisation

04 Embedded AI

Low latency Low power Integrated

01 / Edge vision modules

Low-latency edge vision

Embedded vision modules for teams that need reliable camera-based intelligence without rebuilding the full perception stack from scratch.

Problem: Prototype vision models often fail latency, memory, or integration requirements on target hardware.
Approach: Hardware-aware model design, sensor preprocessing, runtime acceleration, and edge-ready output interfaces.
Use cases: Detection, tracking, inspection, counting, scene state estimation, and local event triggering.
Benefit: Faster path to stable embedded perception with lower cloud dependency.

02 / Multimodal sensor fusion

Robust sensor fusion

Custom pipelines that combine camera, depth, thermal, radar, lidar, or inertial data for more resilient machine understanding.

Problem: Single-sensor systems can degrade under occlusion, poor lighting, motion, range limits, or environmental noise.
Approach: Time alignment, calibration-aware preprocessing, feature fusion, decision fusion, and uncertainty-aware outputs.
Use cases: Autonomous navigation, field robotics, safety monitoring, occupancy sensing, and industrial process awareness.
Benefit: Improved perception reliability across real operating conditions.

03 / AI model optimisation

Edge model optimisation

Compression, acceleration, quantisation, and hardware-aware redesign of perception models for embedded deployment targets.

Problem: Research-grade models are commonly too slow, large, or power hungry for production edge devices.
Approach: Architecture selection, pruning, quantisation, operator replacement, memory planning, and runtime profiling.
Use cases: Optimising detection, segmentation, depth, anomaly detection, and inspection networks.
Benefit: Lower latency and compute cost while preserving application-level performance.

04 / Custom embedded AI systems

Custom embedded AI

System-level development for robotics, industrial automation, smart devices, mobility, and field-deployed platforms.

Problem: Teams need AI perception that integrates with real sensors, firmware, processors, operating environments, and product constraints.
Approach: Requirements definition, sensor architecture, model development, edge runtime implementation, testing, and integration support.
Use cases: Robotic perception modules, intelligent cameras, inspection devices, autonomous field systems, and embedded safety sensing.
Benefit: A coherent perception system designed around the target device and operating context.

Discuss a deployment requirement.

Discuss a Deployment Requirement