Computer Vision: Turn Images and Video into Business Data on AWS

If a person looks at it to decide something, a model can look at it first.

Inspections, counts, verifications and readings from the physical world are often still done by people reviewing images or video by hand. Large image and video archives hold value nobody can search because they lack metadata. And generic AI tools frequently fall short on accuracy for a company's own images.

How DinoCloud works

Every computer vision engagement starts from the decision the image feeds, not from the model. We assess a representative sample of your real images, including the hard cases, agree an accuracy target, and choose the simplest approach that meets it:

• Managed AWS services such as Amazon Rekognition and Amazon Textract
• Multimodal foundation models on Amazon Bedrock
• Pre-trained open models via Amazon SageMaker JumpStart
• Custom models trained on Amazon SageMaker AI when no managed option solves the task

Engagements typically begin with a bounded proof of concept on your own images, followed by a build phase scoped to your needs.

What we can build

• Entity extraction from images: objects, labels, text and attributes returned as structured fields
• Image and document to structured data, combining OCR with multimodal models
• Video understanding: scenes, events or user actions indexed as metadata
• Real-time detection on live camera or sensor streams
• Quality and inspection: defect, damage or anomaly detection
• Custom model training with labeled datasets and evaluation against a baseline

Who it is for

Operations, quality and media teams that review images or video manually at a scale that no longer works, or that tried generic AI tools and need better accuracy. Cameras, edge devices and installation are not included.