AI & Computer Vision
Derive meaningful information from digital images and videos to automate complex visual and operational tasks.
Service Overview
Infygru builds custom artificial intelligence models focused on computer vision, enabling your systems to literally 'see' and interpret visual data. From automated quality control in manufacturing to facial recognition in security systems, we deploy highly accurate neural networks that drastically reduce manual inspection times.
Infygru's AI and Computer Vision team builds production-ready models using PyTorch and TensorFlow, fine-tuned on your proprietary dataset. Our object detection pipelines — based on YOLOv8 and Detectron2 — achieve 95%+ accuracy on manufacturing defect detection, retail shelf compliance, and logistics package counting use cases. Models are deployed as real-time inference APIs using FastAPI and containerized with Docker, integrating directly with your existing camera hardware or ERP systems. We also build LLM-powered document intelligence solutions that extract structured data from invoices, contracts, and government forms.
Every computer vision project at Infygru includes a data strategy phase where we assess training data quality and quantity, implement data augmentation pipelines, and establish model evaluation metrics aligned with your business KPIs. We set up MLOps infrastructure (MLflow, Weights & Biases) for experiment tracking and model versioning, ensuring your AI systems improve over time as new data arrives. Post-deployment, we monitor model drift and retrain models quarterly to maintain accuracy as real-world conditions evolve.
Core Capabilities
How We Work
Discovery Call
We deep-dive into your goals, current stack, and pain points to map out the ideal solution.
Solution Design
Our architects craft a tailored blueprint with timelines, tech stack, and clear milestones.
Agile Delivery
We sprint in 2-week cycles with regular check-ins, ensuring full visibility and fast iteration.
Launch & Support
Go-live with zero downtime. Post-launch, we provide dedicated support and continuous optimization.
Frequently Asked Questions
Q.How much training data do you need for a computer vision model?
For a well-defined object detection task, we typically need 500–2,000 labeled images per class to achieve production-ready accuracy. We can assist with data collection and annotation if needed.
Q.Can your AI models run on edge devices or only in the cloud?
Yes — we optimize models for edge deployment using ONNX, TensorRT, and TFLite, enabling real-time inference on NVIDIA Jetson, Raspberry Pi, and industrial edge computing hardware.