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GENESIS : Genrative AI for Strategy and Industry Solutions


Overview

GENESIS is a multi-agent system designed to streamline the process of identifying and implementing AI/GenAI use cases tailored to specific industries and companies. The system operates through a series of specialized agents that perform individual tasks, gathering insights and generating solutions based on user inputs. Feedback is incorporated throughout the workflow to ensure relevant and practical outcomes.


Architecture Workflow

GENESIS Architecture

1. User Input

  • Objective: Gather the necessary details from the user to customize the GENESIS workflow.
  • Inputs:
    • Mandatory: Industry name and company name.
    • Optional: Specific focus areas such as operations, supply chain, etc.
  • Implementation:
    • A simple web interface (e.g., Flask or Streamlit) or CLI can be used to collect this information.
    • Inputs are validated through predefined dropdowns or regex to ensure correctness.

2. Agent 1: Researcher Agent

  • Task: This agent performs retrieval tasks to gather company-specific and industry-specific insights.
  • Subtasks:
    • Search the web for company profiles, articles, or annual reports.
    • Extract key insights such as company goals, policies, and focus areas.
    • Retrieve policy documents or reports from websites or PDFs (with OCR if necessary).
  • Tools:
    • Web scraping: BeautifulSoup, Selenium, Scrapy.
    • Document processing: PyPDF2, Tesseract OCR.
    • Summarization: OpenAI API, Hugging Face transformers (e.g., BART, Pegasus).
    • Knowledge retrieval: LangChain with vector databases like Pinecone or FAISS.
  • Output:
    • Key company insights and industry trends.
    • Extracted policies or strategic documents.

3. Agent 2: Use Case Generator

  • Task: This agent generates AI/GenAI use cases that align with the company’s strategic goals.
  • Subtasks:
    • Analyze the company’s goals, challenges, and industry trends.
    • Generate a list of AI use cases that could help address these challenges (e.g., improving operations, enhancing customer experience).
  • Tools:
    • Language models: OpenAI API (GPT-3.5/4), Cohere, Anthropic Claude.
    • Template-based generation: Use predefined templates that can be customized with AI-driven suggestions.
  • Output:
    • A set of AI/GenAI use cases with descriptions, linked to the company's needs.

4. Agent 3: Resource Finder

  • Task: This agent identifies and gathers resources required to implement the generated use cases.
  • Subtasks:
    • Search for relevant datasets from platforms like Kaggle or Hugging Face.
    • Identify the required tools, libraries, and APIs needed for implementation.
    • Generate clickable links to access these resources.
  • Tools:
    • APIs: Kaggle API, Hugging Face Datasets API, GitHub search API.
    • Libraries: Requests for web queries, LangChain for semantic search.
    • Automation: Python scripts to gather and present resource links.
  • Output:
    • A structured list of resources, such as datasets, libraries, and tools, necessary to implement the use cases.

5. Agent 4: Report Generator

  • Task: This agent compiles all the collected information into a comprehensive, user-friendly report.
  • Subtasks:
    • Format the insights, use cases, and resources into a well-structured report.
    • Add interactive elements such as hyperlinks to external resources for easy access.
  • Tools:
    • Report generation: Markdown or HTML templates.
    • PDF export: ReportLab, WeasyPrint for generating downloadable reports.
  • Output:
    • A final report in PDF or HTML format, ready for presentation or further analysis.

Tools

  • Web Scraping: BeautifulSoup, Scrapy, Selenium.
  • Data Retrieval: LangChain, Pinecone, FAISS.
  • Language Models: OpenAI GPT-3.5/4, Hugging Face models.
  • APIs: Kaggle, Hugging Face Datasets, GitHub Search.
  • Automation: Python libraries like Requests, PyPDF2.
  • Frontend: Streamlit/Flask for user interaction.

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