Computer Vision & Multimodal
Vision AI from camera to insight.
Real-time inspection, detection and video analytics — plus vision-language models that understand what they see — optimized to run at the edge.
Real-time inspection, detection and video analytics — plus vision-language models that understand what they see — optimized to run at the edge.
From camera capture to real-time edge inference and enterprise integration — a complete pipeline, not a model in a notebook.
IP cameras, industrial sensors and existing video streams — we work with the hardware you already have.
Object detection, segmentation and multi-object tracking tuned to your defects, products and scenes.
Vision-language models and OCR that read scenes, shelves and documents — not just draw boxes around them.
Models optimized with TensorRT and ONNX to run on NVIDIA Jetson in under 50ms — no cloud round-trip.
The system flags uncertain frames for labeling, so accuracy keeps improving on the data you actually see.
Detections become real-time alerts, dashboards and records in your ERP / MES — where decisions get made.
From camera capture to real-time edge inference and enterprise applications — with model lifecycle and governance built in.
Frames stream in from cameras and sensors across your sites — normalized, timestamped and ready for inference.
Optimized detection, segmentation and tracking models run on-device in under 50ms — at production line speed.
Vision-language models and OCR interpret what was detected — products, text, compliance with a planogram or spec.
Results flow into alerts, dashboards and ERP / MES systems, while uncertain frames feed the active-learning loop.
Real-time detection and multimodal understanding, optimized to run at the edge — with model lifecycle and governance.
A fixed-scope path from sample footage to inference running on your line.
We review your footage, cameras and defect classes, and set a measurable detection target.
A trained model runs on your real footage — evaluated against the detection target, on edge hardware.
Deployed in-line with alerts, dashboards and an active-learning loop your team can operate.
Full case study below — including how the active-learning loop keeps accuracy up as new defect types appear.
Manual visual QC was slow, inconsistent and missed subtle surface defects.
Deployed YOLO-based defect detection on edge devices with an active-learning loop to continuously improve on new defect types.
Automated, consistent inspection running in-line at production speed.
Stockouts and poor planogram compliance were quietly costing sales across hundreds of stores.
Vision-language models analyze shelf images for product recognition, gaps and planogram compliance, pushing real-time alerts to store teams.
Better on-shelf availability and measurable recovery of lost sales.
Send us sample footage and your inspection targets — we'll return a feasibility read and edge deployment plan in days.