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Technologies Involved:
PYTHON
Area Of Work: Computer Vision
Project Description

An emerging GenAI-focused enterprise set out to build visual AI applications without traditional model training. Their goal was to solve real-world problems using pre-trained vision models for object detection, captioning, and video analysis. They needed a fast, modular solution to integrate visual models, LLMs, and automation tools into a single workflow-ready web application.
Scope Of Work

The client aimed to apply pre-trained vision models on real-world multimedia data while automating extraction and interaction workflows. They needed a solution to manage image/video sourcing, integrate APIs for inference, and run lightweight, end-to-end applications. The project covered vision model integration, agentic automation, and UI-ready workflow building.
Our Solution

To fulfill the unique vision of this GenAI-driven platform, the project was architected around modularity, automation, and creativity. 

Key Features Delivered:

  • Agentic Workflow Automation: Orchestrated end-to-end job automation using OpenAI Operators and document extraction for tasks like LinkedIn Easy Apply.
  • Visual AI Integrations: Embedded pre-trained models for segmentation, object detection, captioning, VQA, and safety gear recognition using OpenCV and related tools.
  • Media Sourcing Engine: Enabled scraping and sourcing of relevant video/image data from platforms like YouTube and Twitch.
  • Prompt Engineering Layer: Integrated with LLMs and VLMs like GPT and Claude via APIs, enhanced with RAG and embedding strategies for improved results.
  • Structured Data Pipeline: Allowed reuse of extracted image/video metadata across multiple applications including knowledge extraction and visual audit.

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