Computer vision that turns images and video into reliable decisions.
We build detection, classification, and analysis systems for quality control, monitoring, and analytics, trained on your data and deployed to edge or cloud.
What we deliver
From the production line to live video, we turn visual data into structured, actionable decisions with models trained on your environment and deployed where you need them.
- Real-time, consistent visual inspection
- Structured, traceable results
- Edge or cloud deployment
- Continuous improvement via retraining
Computer Vision, end to end
Object Detection & Classification
Real-time detection and classification for quality control, monitoring, and analytics.
Defect & Anomaly Detection
Spot defects and anomalies, including novel ones, with detection and anomaly models.
Image & Video Analysis
Tracking, recognition, and event detection across live streams and archived media.
Edge or Cloud Inference
Low-latency edge inference at the source, or scalable cloud inference, with continuous retraining.
Capabilities that power this solution
See it in production
Computer Vision / Affective AIEyeQ — Engagement Detection
EdTech & Research
EyeQ reads a learner's engagement from video, fusing three custom models (facial emotion, head pose and body pose) behind a FastAPI service that scores each frame and logs the result per student. The models were custom-trained on Amazon SageMaker, and the system was used in peer-reviewed research.
Document AI / OCRHybrid Document Extraction (VLM + OCR)
Fintech & Document Automation
Our own document-extraction technique that fuses an open-source vision-language model, PaddleOCR and classical computer vision to turn messy real-world documents (receipts, invoices, cheques, forms) into clean, structured data, running entirely on open-source models with no third-party API.
Frequently asked questions
It varies by task. We often start with a modest labeled set, use pre-trained backbones and augmentation, then improve accuracy over time with an active-learning loop on real production data.
Yes. We deploy optimized models to edge devices for low-latency inference at the source, syncing results to a central service for analytics and retraining.
We combine supervised detection with anomaly detection to flag unfamiliar defects, then fold them into retraining so the system keeps improving.
Let's build your AI advantage.
Book a strategy call and walk away with a clear, technical plan, whether you build custom or start from an accelerator.