SupportPilot AI

AI-powered support operations assistant that analyzes tickets, retrieves similar incidents, recommends routing, and suggests resolutions using GPT-5.6 and RAG.

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🚀 SupportPilot AI

Inspiration

Working as an IT support engineer, I noticed that a significant amount of time is spent simply routing incidents to the correct support team. Engineers manually read incident descriptions, search for similar historical tickets, identify the appropriate Assignment Group, and finally assign the ticket. Incorrect routing leads to SLA violations, delayed resolutions, and unnecessary manual effort.

I wanted to build an AI assistant that doesn't replace engineers—it assists them by making intelligent recommendations while keeping humans in control.

That idea became SupportPilot AI.

What it does

SupportPilot AI is an AI-powered incident routing and resolution assistant for ServiceNow.

When a new incident or catalog task arrives, the application:

  • Retrieves similar historical incidents using semantic search.
  • Uses GPT-5.6 to analyze the current ticket together with the retrieved context.
  • Recommends the most appropriate Assignment Group and Business Service.
  • Suggests a likely resolution.
  • Provides a confidence score and explainable reasoning.
  • Allows engineers to approve or override the recommendation.
  • Automatically updates ServiceNow with structured work notes, maintaining a complete audit trail.

This keeps engineers in control while significantly reducing manual analysis and routing effort.

How I built it

SupportPilot AI combines enterprise integration with modern AI techniques.

Tech Stack

  • Python
  • Streamlit
  • OpenAI GPT-5.6
  • OpenAI Embeddings
  • ChromaDB
  • ServiceNow REST APIs
  • Pandas

AI Workflow

  1. Fetch live incidents from ServiceNow.
  2. Generate embeddings for the incoming incident.
  3. Retrieve similar historical incidents from ChromaDB.
  4. Pass the retrieved evidence to GPT-5.6.
  5. Generate:
    • Assignment Group recommendation
    • Business Service recommendation
    • Resolution suggestion
    • Confidence score
    • Explainable reasoning
  6. Allow the engineer to approve or override the recommendation.
  7. Update ServiceNow with the final decision and structured work notes.

This Retrieval-Augmented Generation (RAG) approach grounds GPT-5.6's reasoning in historical enterprise data instead of relying only on the model's general knowledge.

How I used GPT-5.6

GPT-5.6 is the reasoning engine behind SupportPilot AI.

It performs two key enterprise tasks:

  • Intelligent incident routing by recommending the correct Assignment Group and Business Service.
  • Resolution recommendation based on the current incident and retrieved historical evidence.

Rather than producing a simple answer, GPT-5.6 also generates confidence scores and reasoning, making its recommendations transparent and easier for engineers to trust.

How I used OpenAI Codex

OpenAI Codex played a major role throughout development.

I used Codex to:

  • Implement ServiceNow integration.
  • Build the human approval and override workflow.
  • Debug REST API issues.
  • Improve Streamlit UI and user experience.
  • Refine structured AI outputs.
  • Optimize the overall application during rapid iteration.

Using Codex significantly accelerated development, allowing me to focus more on solving the enterprise problem than on repetitive implementation tasks.

Challenges I ran into

The biggest challenge was integrating AI into a real enterprise workflow rather than building a standalone chatbot.

Some of the technical challenges included:

  • Designing an effective Retrieval-Augmented Generation pipeline.
  • Working with ServiceNow APIs and different record types such as Incidents and Catalog Tasks.
  • Building a reliable approval workflow that preserves human oversight.
  • Generating explainable AI recommendations instead of black-box outputs.
  • Ensuring ServiceNow updates created meaningful audit trails.

These challenges pushed me to think beyond model integration and focus on building a production-oriented workflow.

What I learned

This project taught me that enterprise AI is about much more than calling an LLM.

Successful AI systems require:

  • Reliable retrieval of relevant knowledge.
  • Explainable recommendations.
  • Human oversight.
  • Seamless integration with existing enterprise platforms.
  • Well-designed user experiences that build trust.

I also gained practical experience integrating GPT-5.6 into a real business workflow and learned how tools like OpenAI Codex can dramatically accelerate development.

What's next

SupportPilot AI has the potential to evolve into a complete AI operations platform.

Future enhancements include:

  • Knowledge Base integration
  • Multi-agent workflows
  • Automatic incident summarization
  • Teams and Slack notifications
  • Continuous learning from engineer feedback
  • Predictive incident categorization
  • Autonomous resolution for repetitive issues

Why this project matters

SupportPilot AI demonstrates how modern AI can augment enterprise support teams—not by replacing engineers, but by helping them make faster, better-informed decisions.

By combining GPT-5.6, Retrieval-Augmented Generation, ServiceNow integration, and human-in-the-loop governance, the project shows a practical approach to bringing trustworthy AI into real-world IT operations.

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