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No-Code Computer Vision Platform

We built a no-code computer vision platform that enables users to create, train, and deploy custom AI models without writing a single line of code. The system simplifies the entire computer vision workflow  from data upload and model selection to training, testing, and deployment  all through a user-friendly interface designed for non-technical teams.

Summary :

We have developed an interactive, no-code AI platform that empowers users to build vision-based models for object detection, classification, and segmentation.
The platform abstracts away technical complexity, providing a drag-and-drop interface for dataset management, model training, and API-based deployment.
It makes AI accessible to business users, researchers, and developers alike , drastically reducing the time and effort required to turn visual data into actionable intelligence.

Problem

Problem

Objectives

  • Democratize Computer Vision

    Enable anyone to build AI vision models without coding.

  • Simplify Workflows

    Combine data upload, training, and deployment in one platform.

  • Reduce Development Time

    Cut down AI setup from weeks to hours.

  • Improve Accessibility

    Support non-technical users in deploying models easily.

  • Provide Flexibility

    Allow model customization and export for multiple use cases.

Challenges

  • Designing a simple yet powerful user interface for complex ML tasks.
  • Ensuring accuracy and performance across diverse datasets.
  • Managing compute resources efficiently for on-demand model training.
  • Providing real-time feedback and error handling in the UI.
  • Supporting multiple vision use cases (detection, segmentation, classification).

Solution

We built a modular, no-code computer vision platform that guides users through each stage of model creation , from data import to deployment.
Users can upload datasets, label images, select pre-trained models (like YOLO or ResNet), and initiate training directly through the interface.
The system monitors progress, visualizes results, and auto-generates deployment-ready APIs.
It also supports integration with edge devices and cloud environments, making it scalable for both R&D and enterprise use.

Architecture

Data Upload & Labeling
Users upload image datasets and label objects via the web interface.
Model Configuration
Choose from pre-built architectures or customize hyperparameters.
Training & Validation
Models train automatically on GPUs with live progress tracking.
Evaluation & Visualization
Accuracy and confusion matrices shown instantly.
Deployment
Generate inference APIs or deploy models to the cloud or edge devices.

Results & Impact

Reduced model development time from weeks to hours

Enabled non-technical users to create high-performing vision models

Increased AI adoption across teams through accessible design

Simplified training-to-deployment pipeline within a single interface

Supported multiple domains , retail, healthcare, construction, and manufacturing

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Cornelius | Founder, Shatterpoint
Cornelius | Founder, Shatterpoint
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Vineet | Gauss Moto Inc
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