Fission Labs Computer Vision and Multimodal AI on AWS

Manual visual inspection is slow, inconsistent, and hard to scale across lines, shifts, and sites. Fission Labs designs and deploys computer vision and multimodal AI on AWS that detects defects, classifies objects, and interprets images and video, with accuracy and false positive rates you can measure.

Why computer vision and multimodal AI on AWS Off the shelf vision APIs rarely handle look alike parts, glare, or rare defects. Custom models trained on your images, combined with vision language models that can reason about what they see, close that gap. Fission Labs, an AWS Advanced Tier Services Partner with the AWS Generative AI Competency, builds these systems on Amazon SageMaker and Amazon Bedrock and runs them in production.

What you get: inspection you can rely on

• Dataset preparation: class design, annotation with Amazon SageMaker Ground Truth, and handling of noise, glare, and class imbalance
• Custom vision models: object detection, segmentation, and classification with PyTorch and YOLO, trained on Amazon SageMaker
• Vision language models: multimodal models on Amazon Bedrock to describe, verify, and explain what the camera sees
• Video analytics: frame level detection and fusion across video streams for continuous monitoring
• Production endpoints: low latency inference in the cloud or at the edge, tuned to agreed false positive rates
• DevOps for scale: CI/CD, monitoring, and retraining pipelines so models scale across lines and sites

Proven results: surgical instrument identification For a medical device client, we built a YOLOv8 model on Amazon SageMaker to identify nearly identical surgical instruments, including a dataset pipeline for class consolidation, glare, and annotation quality.

• Accuracy improved from a 28% baseline to 65% on sample data in the proof of concept
• Validated path toward 80%+ accuracy in the next phase

Security and governance Images, video, and model weights stay in your AWS account, encrypted with AWS KMS and controlled through AWS IAM. Datasets, model versions, and evaluation results are tracked, so every prediction can be traced to the model that made it.

How we engage

• Assess: review sample images or video, defect types, and accuracy targets
• Prepare data: design classes, annotate, and build training and test sets
• Build and train: develop vision and multimodal models and compare approaches
• Validate: measure accuracy, false positive rate, and latency with your quality team
• Deploy and scale: release production endpoints with monitoring, retraining, and runbooks

Who this is for

• Manufacturing, inspection, and quality assurance teams
• Medical device, life sciences, and logistics teams verifying parts and kits
• Organizations moving a vision pilot into production across sites

Get started Contact us for a free scoping call to review sample images, target defects, and accuracy goals.