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Self-improving Amalgamation AI

Amalgamation AI is an “AI engineer” that automatically builds custom vision systems, cutting cost, time, and expertise needed for high-accuracy tasks.

Abstract

Building custom, high-performance, and cost-effective computer vision systems is slow, expensive, and requires deep expertise. Off-the-shelf models often fail on real-world tasks that involve unusual objects, reasoning, or counting, and many industrial use cases demand extremely high accuracy. Most companies lack the in-house talent to design complex AI pipelines. Amalgamation AI acts as the “AI engineer,” automatically generating solutions from natural language task descriptions. This lowers the technical barrier and makes large-scale deployment of vision applications feasible and affordable.

Challenges

Real-world problems typically require pipelines of multiple models, which are currently hand-crafted by expert engineers, a costly and difficult process. Also, current AI systems also rely on large amounts of labeled data, and there are no integrated tools that can build strong models from only a few examples or guide teams on which data is most valuable to label. As a result, companies are forced into inefficient, brute-force labeling of thousands of images.

The benefits of Self-improving Amalgamation AI

Lower Overall Costs	Reduces expenses across development, deployment, and maintenance.
Faster Results	Shortens the time from business idea to deployed solution, accelerating ROI and competitive edge.
Solves Niche Problems	Handles unique, specialized use cases that generic AI models can’t address effectively.
Empowers Teams	Enables non-experts to build powerful AI solutions, removing dependence on scarce specialist talent.

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Use Cases

End users turn to Amalgamation AI when they need to automate unique, high-stakes visual tasks for which no accurate off-the-shelf solution exists and traditional AI projects would be too slow or expensive.

• Quality Control in Manufacturing
  • A medical device factory discovers a subtle new defect that existing vision systems can’t detect. With only 5 example images, the QC manager gives the task to Amalgamation AI. Within hours, a working prototype is created. Over the following days, the system improves through quick feedback loops and is deployed.
  • Value: A critical inspection task is automated in under two weeks, avoiding production shutdowns and costly recalls with minimal engineering effort.

• Automating Infrastructure Assessment

  • A utility company must inspect 50,000 utility poles across a vast region. Traditional human inspections are slow, and a single AI model can’t handle the variety of issues. Amalgamation AI builds the right pipeline automatically, using a few examples and simple task descriptions. As drones collect new data, the system continuously learns and adapts.
  • Value: Infrastructure inspection is transformed from a slow, manual process into a fast, data-driven operation, improving safety while reducing costs.

• Verifying High-Value Second-Hand Goods

  • An online luxury marketplace struggles to authenticate hundreds of handbags daily, a process that normally requires years of expert training. Amalgamation AI captures expert knowledge through images and natural language, then builds a multi-step inspection assistant that mimics expert reasoning.
  • Value: The marketplace scales authentication, empowers staff to operate at near-expert levels, boosts throughput, and increases customer trust.

Case studies: Proven Value for real case

These example projects demonstrate Amalgamation AI’s ability to deliver exceptional results quickly.

• Industrial Anomaly Detection
  • Detected anomalies of water, device component, coals with high accuracy using only few reference images.
• Document Validation
  • Verified seals, stamps, and signatures with perfect accuracy using prompts alone (no reference data).
• OCR on Metal
  • Accurately read serial numbers on metal bullions with a small model and only prompts.
• Infrastructure Monitoring
  • Read analog meters and detected fires accurately without prior training images.
• Real-Time Wild Animal Detection
  • Successful real-time detection on a compact model.
• Manufacturing Defect Detection
  • Demonstrated high-precision, real-time defect detection and notifications.
• Incident Detection
  • Detected factory abnormalities (e.g., alarms, falling objects) successfully.

Trial

• Demo App：Try our demo app on Web

• PoC

Related Information

• Newspaper
  • Whale detection in Hachijo-town: Nikkei Newspaper (in Japanese)
• Others
  • Fujitsu to provide the world’s first enterprise-wide generative AI framework technology to meet changing needs of companies

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