README.md

metadata

title: SupportMind
emoji: 🧠
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: false

AetherFlow AI | SupportMind Engine 🧠

Confidence-Gated Support Intelligence for B2B SaaS Customer Operations

"B2B SaaS support teams don't lose customers because agents are slow. They lose them because AI acts with false confidence on ambiguous tickets — and nobody in the stack knows it happened."

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🎯 What is SupportMind?

SupportMind is a confidence-gated, uncertainty-aware ticket routing system that solves the most expensive unsolved problem in B2B SaaS support: AI routing ambiguous tickets with false certainty.

Unlike traditional AI solutions (Zoho Zia, Freshworks Freddy, Zendesk, and Salesforce Einstein) which use standard Softmax classifiers with no uncertainty output, SupportMind implements Monte Carlo Dropout on DistilBERT. This produces calibrated confidence scores and Shannon entropy, enabling a robust three-tier decision gate:

Action	Confidence	Entropy	What Happens
ROUTE	≥ 0.80	≤ 0.35	Auto-assign to the correct agent queue immediately.
CLARIFY	0.55 – 0.80	N/A	Ask 1 targeted, high-information-gain question to disambiguate.
ESCALATE	< 0.55	N/A	Flag as complex; send to human triage immediately.

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🏗️ Detailed System Architecture

The SupportMind engine operates as a multi-stage pipeline designed to mimic human cognitive processes in support triage:

Stage 1: Feature Extraction & Signal Detection

When a ticket arrives, it passes through an NLP feature extraction layer:

• DistilBERT Embeddings: Extracts deep semantic meaning (768-dimensional space).
• VADER Sentiment Analysis: Measures emotional tone (frustration, anger).
• Regex & Heuristics: Detects urgency flags ("ASAP", "System Down") and text complexity (Flesch-Kincaid).

Stage 2: Confidence-Gated Router (MC Dropout)

Instead of a single forward pass, the DistilBERT classifier performs 20 stochastic forward passes. By randomly deactivating neurons, it generates a distribution of predictions.

• Low variance across passes = High Confidence (Safe to Route)
• High variance across passes = High Epistemic Uncertainty (Needs Clarification or Escalation)

Stage 3: The Intelligence Layer

• SLA Breach Predictor (XGBoost): Evaluates the extracted features against current queue depth and historical SLA data to predict the probability of missing SLA targets (AUC 0.83).

• Clarification Engine (Hybrid Architecture): When the router enters the CLARIFY tier, the engine uses a two-layer approach:

  1. LLM Layer (Groq LLaMA3-8B): Generates a ticket-specific question referencing the customer's exact words. Runs in ~100ms via Groq's optimized inference.
  2. Template Layer (fallback): If LLM is unavailable, selects from 47 pre-built templates scored by expected Shannon entropy reduction (information gain).

  This design ensures the system never stops routing — LLM enhances quality when available, templates guarantee reliability always.

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📊 Benchmark Results (Honest Dual-Evaluation)

⚠️ Benchmark Validity: To ensure complete transparency, this project reports two sets of accuracy numbers:

1. In-Distribution (Synthetic): The test set is generated from the same templates as the training data. The 100% accuracy here merely confirms the model successfully learned the training distribution without catastrophic forgetting.
2. Out-of-Distribution (OOD): Evaluated against a separate, hand-crafted dataset of 96 real-world-style tickets (informal language, typos, missing context, and ambiguous edge-cases). This is the honest estimate of the model's true generalization ability before fine-tuning on real production data.

Metric	In-Distribution (synthetic)	Out-of-Distribution (hand-crafted)
Overall Routing Accuracy	100.0%	57.3%
Precision on Auto-Routed	100.0%	100.0%
Accuracy on Ambiguous Tickets	—	30.0%

Why the OOD Accuracy is "Low" (And Why That's Good)

On the OOD dataset, the model correctly routed the familiar tickets but struggled with the novel/ambiguous ones. However, it only auto-routed 2.1% of the OOD tickets (achieving 100% precision on those). It correctly flagged the remaining 97.9% as requiring clarification (51%) or escalation (47%).

Traditional Softmax classifiers would have blindly auto-routed these unfamiliar tickets, leading to costly misroutes. SupportMind's confidence gate correctly prevented these misroutes. This proves the architecture works as intended—even when the model weights are untrained for the specific domain, the system fails safely.

Why This Matters for Zoho Desk + Zia

Zia's current field prediction uses standard Softmax — it returns a category with no uncertainty signal. When Zia is wrong on an ambiguous ticket, the agent only discovers the misroute after picking it up. SupportMind's clarification gate catches this before routing, reducing misroute cost from agent-time to one extra customer message.

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🚀 Installation & Setup Guide

Follow these steps to set up the engine locally for development or demonstration.

1. Prerequisites

• Python 3.10+
• Git
• Virtual Environment tool (venv, conda, etc.)

2. Clone and Install

# Clone the repository
git clone https://github.com/asmitha2025/supportfloww.git
cd supportfloww

# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

3. Running the System Locally

The core system is powered by FastAPI, serving both the REST API and the interactive dashboard.

# Start the FastAPI server
cd src
uvicorn api:app --host 0.0.0.0 --port 7860 --reload

Once the server is running, navigate to http://localhost:7860/ in your web browser to access the Live SupportMind Dashboard.

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🧠 Training the Models

If you wish to retrain the models from scratch using your own datasets:

1. Prepare Data: Place your raw ticket data in data/raw/.
2. Train Router:

   python src/train_baseline.py  # Trains the fallback TF-IDF + Logistic Regression model
   python src/train_router.py    # Trains the DistilBERT sequence classifier
   

   These scripts train the routing models and save them to models/ticket_classifier/.
3. Train SLA Predictor:

   python src/train_sla.py
   

   This trains the XGBoost model based on synthetic feature data and saves to models/sla_predictor/.
4. Evaluate System:

   python src/evaluate.py
   

   Generates benchmark metrics comparing MC Dropout against a standard Softmax baseline.

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📡 Comprehensive API Reference

SupportMind exposes a fully documented RESTful API. When the server is running, visit http://localhost:7860/docs for the interactive Swagger UI.

POST /route

Description: Main routing endpoint. Processes a ticket and returns a 3-tier confidence-gated decision. Request Body:

{
  "text": "The API endpoint /v2/export returns a 500 error when batch size exceeds 1000.",
  "customer_id": "cust_8910"
}

Response:

{
  "action": "route",
  "confidence": 0.942,
  "entropy": 0.12,
  "top_category": "technical_support",
  "features": {
    "sentiment_score": -0.25,
    "urgency_flags": []
  },
  "sla_breach_probability": 0.15,
  "latency_ms": 45.2
}

POST /clarify

Description: Fetch the best clarification question based on model uncertainty.

POST /sla/predict

Description: Predict SLA breach risk independently based on features.

POST /churn/signal

Description: Extract churn signals from an array of historical thread texts.

GET /metrics

Description: Live system health and routing distribution statistics.

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🐳 Docker Deployment

For production deployments, package the application using Docker.

# Build the image
docker build -t supportmind .

# Run the container
docker run -d -p 7860:7860 --name supportmind-api supportmind

For advanced orchestration, we recommend extending the deployment with docker-compose to include Redis or RabbitMQ for asynchronous webhook processing.

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📁 Repository Structure

supportmind/
├── src/
│   ├── api.py                    # FastAPI server & endpoints
│   ├── confidence_router.py      # DistilBERT MC Dropout logic
│   ├── clarification_engine.py   # Shannon entropy info-gain logic
│   ├── sla_predictor.py          # XGBoost SLA modeling
│   ├── feature_extraction.py     # NLP Feature engineering
│   ├── churn_extractor.py        # Sentiment & Churn analysis
│   ├── train_router.py           # DistilBERT training script
│   ├── train_sla.py              # XGBoost training script
│   └── evaluate.py               # Evaluation & benchmark suite
├── dashboard/
│   └── web/                      # Interactive Frontend HTML/CSS/JS
│       ├── index.html            # Main UI
│       ├── app.js                # Frontend logic & API calls
│       └── style.css             # Glassmorphism styling
├── data/
│   └── clarification_bank.json   # 47 Question templates
├── models/                       # Stored model weights (ignored in git)
├── tests/                        # Pytest suite
├── Dockerfile                    # Containerization instructions
├── requirements.txt              # Python dependencies
└── README.md                     # You are here

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👤 Author

Asmitha · BSc Data Science · 2026

Part of the three-project portfolio arc:

1. OPTI-FAB → Manufacturing edge AI with confidence gating
2. IncidentMind → RL-based incident response with ambiguity awareness
3. SupportMind → NLP ticket routing with MC Dropout uncertainty

"I spent the last year building systems that know what they don't know."