Google Vision AI: Why Computer Vision Matters for Automation
Google Vision AI spans the Cloud Vision API (images: labels, OCR, safe search, object localisation) and Vertex AI Vision (video streams, occupancy analytics, motion detection). For automation, vision is what turns physical and visual data into structured inputs for agentic workflows — and different sectors need it in different ways.
What Google Vision offers
The Vision API provides label detection, OCR, safe search, and object localisation on images — with improved models available via builtin/latest. Vertex AI Vision adds real-time video analysis, occupancy analytics, motion detection zones, and tools like vaictl for visualising model outputs. Vision Warehouse supports semantic and similarity search over video and images at scale.
Why each use case needs vision differently
Manufacturing: Defect detection on production lines needs high-precision, low-latency image classification and localisation — often with custom models trained on your product. Vision feeds our quality-control agents so they can flag and route in real time.
Retail: Shelf analysis, stock levels, and planogram compliance rely on object detection and scene understanding. Vision powers agents that monitor shelves and trigger reorders or alerts.
PropTech: Property condition, damage assessment, and compliance checks (e.g. fire doors, signage) use image and sometimes video. Vision turns site visits into structured data for lease and maintenance workflows.
Healthcare: Triage and routing of medical imaging (e.g. X-rays, scans) use specialised models; Vision API and Vertex can support non-diagnostic tasks like document capture and form extraction in patient intake.
How ConvertToAI uses vision
We integrate Google Vision (and, where appropriate, other vision models) into our agentic platform so that document and image-heavy workflows get one pipeline: extract from PDFs and images, classify and validate, then hand off to LLM agents for reasoning and action. Vision is one input modality; the agent decides when to call it and how to use the result — keeping guardrails and audit trails consistent across text and vision.