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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

industry

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

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

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

  • PoC