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  Query

AI-Powered Customer Support Automation System

Inspiration

Handling customer support at scale is a significant challenge for growing SaaS businesses like ABC Technologies. The inspiration for this project stemmed from the bottleneck created by manual ticket handling, which often leads to delayed responses and increased operational costs. We wanted to design an intelligent, context-aware system that could autonomously categorize, route, and resolve customer queries while knowing exactly when to escalate sensitive issues to human supervisors.

What it does

The AI-Powered Customer Support Automation System is a LangGraph-based workflow that processes customer requests intelligently. It can:

• Classify Intents: Automatically categorize queries into Sales, Technical, Billing, or Account issues.
• Route Intelligently: Direct requests to specialized AI agents tailored to handle specific departmental needs.
• Provide Context-Aware Answers: Use a RAG (Retrieval-Augmented Generation) pipeline to fetch accurate answers from the Company Policy Document, Pricing Guide, Technical Manual, and FAQ Document.
• Remember Past Interactions: Leverage SQLite-based memory to maintain conversation history, allowing the AI to follow up seamlessly on past issues.
• Human-in-the-Loop Validation: Escalate high-risk requests—like refunds, subscription cancellations, or account closures—to a human supervisor before taking action.
• Ensure Quality Assurance: Use a Supervisor agent to validate and polish final responses before they reach the customer.

Built with

• Python
• LangGraph
• LangChain
• Streamlit
• SQLite
• ChromaDB
• Groq API

How we built it

The core engine of the system was built using LangGraph, enabling a stateful, multi-agent workflow. We defined a robust State structure to track query details, retrieved context, and approval status throughout the graph execution.

• Agents: We developed specialized agents for Intent Classification, Sales, Technical Support, Billing, and Account Management.
• Memory: We integrated SQLite as the memory backend to store and retrieve state information across sessions.
• RAG Pipeline: We implemented a RAG pipeline utilizing document embeddings and vector search to pull relevant information from the provided knowledge base.
• Conditional Routing: We utilized LangGraph's conditional edges to handle routing based on intent classification and human-in-the-loop requirements.

Challenges we ran into

• State Management in LangGraph: Ensuring the state reliably carried all necessary context (conversation history, retrieved documents, approval flags) without becoming overly bloated or causing unexpected overwrites.
• Accurate Intent Classification: Fine-tuning the intent classifier to accurately distinguish between nuanced queries, especially when a query contained overlapping themes (e.g., a technical error related to a billing page).
• Human-in-the-Loop Integration: Pausing the LangGraph execution midway to wait for human supervisor input and then cleanly resuming the workflow for high-risk actions.
• Memory Persistence: Configuring the SQLite checkpointing properly to recall specific user histories across completely separate query sessions.

Accomplishments that we're proud of

• Successfully orchestrating a multi-agent workflow where specialized agents seamlessly hand off tasks.
• Building a robust Human-in-the-Loop mechanism that accurately flags sensitive operations without disrupting the automated flow for standard queries.
• Achieving high accuracy in the RAG pipeline, ensuring the AI answers only based on the provided company documents.

What we learned

• The immense power and flexibility of LangGraph for building complex, cyclical, and stateful AI applications compared to standard linear chains.
• Best practices for designing memory checkpoints and state persistence using SQLite.
• How to effectively implement guardrails and supervisor nodes to ensure AI responses remain safe, polite, and aligned with company policies.

What's next for the Support Automation System

• Omnichannel Integration: Connecting the system directly to email, Slack, and live chat widgets.
• Advanced Analytics Dashboard: Building a UI for supervisors to easily review flagged requests, approve them with one click, and view metrics on AI resolution rates.
• Multi-language Support: Upgrading the agents to seamlessly handle and reply to queries in multiple languages.
• Self-improving Knowledge Base: Implementing a feedback loop where the AI suggests new FAQ entries based on recurring unresolved customer questions.

Built With

• api
• chromadb
• groq
• langchain
• langgraph
• python
• sqlite
• streamlit