Skip to main content

Public

Project privacy set to public. By default, its content is available to everyone (authenticated or not). Please note that more restrictive permissions might exist on some items.

    •  
      code_for_government #483398
    New Feature development
    Academic Bank of Credits
    AI-Based Automated Onboarding Advisor for ABC/NAD Integration – Academic Bank of Credits
    Divyansh Jha (divyanshjha)
    Active
    Python, AI Workflow Design, Chatbot, REST APIs
    Medium
    Empty

    This task focuses on creating an AI-based onboarding assistant to streamline and automate the institution onboarding process for the Academic Bank of Credits (ABC) and NAD-DigiLocker ecosystem. The goal is to reduce manual intervention, improve accuracy, and ensure faster eligibility verification for institutions by intelligently guiding them through each step of the onboarding journey.

    The onboarding advisor should use AI to pre-screen registrations, validate institutional data from authoritative databases (UGC, AICTE, NCVT, etc.), and dynamically generate contextual prompts to assess institutional readiness (e.g., data format used, ABC ID integration, digital certificate issuance).

    By automating common checks and capturing readiness indicators early in the process, the system can help filter unqualified or duplicate registrations, assist new institutions with next steps, and reduce the load on human reviewers. This tool should support facial recognition or document scanning (UGC/Autonomy Letters), and auto-fetch data using AISHE codes to pre-fill institutional details.

    • Develop an intelligent onboarding flow with:

      • AI-powered prompts that ask contextually relevant questions based on the user's profile or input (e.g., "Do you maintain data in CSV or XML?" or "Have you issued ABC IDs to your students?")

      • Pre-validation of institutional registration data using public databases (UGC, AICTE, NCVT, etc.) and AISHE code-based lookups.

      • Duplicate or invalid registration detection using NLP and pattern matching.

      • Facial recognition module or document scanning for verifying official documents (optional module).

    • Integrate a lightweight chatbot or conversational UI to assist institutions in real-time.

    • Provide real-time recommendations on steps needed to complete onboarding (e.g., "Digitize historical student records", "Generate ABC IDs").

    • Log responses for audit and dashboard summarization.

    A working onboarding advisor tool that:

    • Screens institutions for eligibility using predefined checks and AI logic.

    • Interacts with users using dynamic question trees to assess readiness.

    • Validates institution metadata from authoritative sources using AISHE code or API integrations.

    • Helps avoid duplicate registrations and incorrect submissions.

    • Includes a simple front-end interface (CLI or Web-based) and back-end logic that can be deployed on NAD/ABC platforms.

    • Comes with sample flows, test cases, README, and deployment guide.

    Submit tool scripts via Git. Functional workflow in a staging environment + chatbot training data

    https://openforge.gov.in/plugins/git/abc-collabwithgov/abc-onboarding-assistant.git

    Empty
    2025-05-16 16:16
    2025-10-31

    Organization Type: Government Publisher Name: Academic Bank of Credits (Digital India Corporation)

    Sanjay Patel (sanjay_patel)
    Empty
    Empty
    #483398
    Display settings

    Follow-ups

    User avatar
    prakhar pragyan chaubey (prakharp)2025-12-24 21:19

    Hello, I am interested in working on this project. Could you please provide the link to the GitHub repository?

    My Proposed Approach:

    Hybrid Logic Core: Implement a deterministic state machine for the onboarding steps to ensure strict regulatory compliance, while using an NLP layer (e.g., LangChain) specifically for handling user queries and context extraction.

Automated Verification: Build an asynchronous middleware to validate AISHE codes against UGC/AICTE APIs in real-time, preventing duplicate registrations before the workflow begins.

Dynamic Workflow: Design the chatbot to fetch institutional metadata first, then use that data to prune the question tree—ensuring users are only asked relevant questions based on their institution type (e.g., technical vs. general).
    User avatar
    Amit Kumar (amitkr12)2025-09-23 12:28
    • Mentor(s) Amit Kumar (amitkr12) removed
    • Last Submission Date changed from 2025-06-30 to 2025-10-31
    User avatar
    Amit Kumar (amitkr12)2025-09-23 12:13
    • Assign To changed from None to Divyansh Jha (divyanshjha)
    • Assigned On changed from 2025-05-16 to 2025-09-23
    User avatar
    Sanjay Patel (sanjay_patel)2025-08-07 15:05
    • Repository URL
      Something went wrong, the follow up content couldn't be loaded
      Only formatting have been changed, you should switch to markup to see the changes
    User avatar
    Aryaman Gupta (aryaman0001)2025-06-19 11:41

    Hello Sir, I am very interested in working on this project. I have good experience building chatbots and automating with AI and NLP. And expertise in making rag solutions.