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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.

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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.

Technologies

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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Amit Yadav
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Founder, Gemperts India Pvt Ltd
“My experience with this project showcased the team’s strong expertise in computer vision and depth estimation. They provided a complete solution, including the app and backend, ensuring seamless integration and high performance. “
Billy Brush
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Founder, Darkhorse.AI
“The team's attention to detail in data annotation was evident throughout the project. They provided accurately labeled datasets with consistency, ensuring smooth AI model training. Their structured approach made the entire process efficient and reliable. “
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Vision AI
Generative AI
Edge AI
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Intelligence
Data Engineering
AI Product Design
Data Annotation
AI Impact
Healthcare & Life Sciences
Financial Services & Insurance
Manufacturing & Industry
Transportation & Logistics
Other Industries →
Connect with us
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About us
Contact us
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Made with ❤️ in India and Serving Worldwide.
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USA | UK | AUSTRALIA | SINGAPORE | UAE | EUROPE

Copyright © 2025 Brainy Neurals. All Rights Reserved