Uncertainty-Aware Neural Engine

AI that knows when it doesn't know

MC Dropout uncertainty quantification on DistilBERT. Three-tier routing: Route · Clarify · Escalate. Optimize support workflows with surgical precision.

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pipeline_status: active
account_tree
Tier 1: Auto-Route
Confidence > 0.92
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psychology
Tier 2: Clarification
Ambiguity Detected
support_agent
Tier 3: Escalate
Human Protocol
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OOD Routing Accuracy
0%
trending_up 100% on auto-routed
Auto-Route Precision
0%
Zero false auto-routes
Safe Failure Rate
0%
Flagged for human review
Pipeline Latency
0ms
DistilBERT Optimized
Interactive

Live Ticket Router

Type a support ticket and watch the confidence-gated router decide in real-time

Try:
🎯

Enter a ticket and click "Route Ticket" to see the confidence-gated decision

Technical

System Architecture

Three-stage pipeline with MC Dropout confidence gating

1
Feature Extraction
DistilBERT VADER

768-dim embedding + sentiment + urgency

→
2
Confidence-Gated Router
MC Dropout Shannon Entropy

3-tier decision gate (20 stochastic passes)

→
3
Intelligence Layer
XGBoost SLA Churn Signal

SLA breach prediction (AUC 0.83)

Monte Carlo Dropout — 20 Stochastic Forward Passes

Each pass randomly deactivates different neurons, producing a distribution of predictions instead of a single overconfident output.

Active neuron Dropped out

Competitor Architecture Gap

Platform AI Feature Handles Ambiguity? Clarification? Entropy Output?
Zoho DeskZia Field PredictionsBinary failNoNo
FreshdeskFreddy Auto TriageMajority-class defaultNoNo
ZendeskIntelligent TriageGeneral queue fallbackNoStatic only
SalesforceEinstein ClassificationFails on unstructuredNoNo
SupportMindConfidence-Gated Router3-tier gate47 templatesReal-time Shannon
Results

Honest Dual-Evaluation

Transparent benchmarks: in-distribution synthetic data + out-of-distribution hand-crafted tickets

Why two sets of numbers? The In-Distribution set (100% accuracy) confirms the model learned its training distribution. The Out-of-Distribution set (57.3% accuracy on 96 hand-crafted tickets) is the honest estimate of real-world generalization. On OOD data, the model auto-routed only 2.1% of tickets (with 100% precision) and safely flagged the rest for human review.
Overall Routing Accuracy
In-Distribution (synthetic)
100.0%
Out-of-Distribution (OOD)
57.3%
OOD = honest generalization estimate
Precision on Auto-Routed
In-Distribution
100.0%
Out-of-Distribution
100.0%
Zero false auto-routes on novel data
OOD Routing Gate Distribution
Auto-Routed (safe)
2.1%
Clarify (flagged)
51.0%
Escalated (flagged)
46.9%
97.9% safely flagged for human review
OOD Ambiguous Accuracy
Hand-crafted ambiguous tickets
30.0%
Model correctly defers these to clarification

Real-Time System Insights

Live Metrics
Model Engine
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Routing Distribution
Route Clarify Escalate
Total Requests
0
Developer

API Reference

RESTful API with FastAPI — complete documentation at /docs

POST
/route

Main routing endpoint — returns 3-tier confidence-gated decision

POST
/clarify

Get best clarification question for uncertain ticket

POST
/sla/predict

Predict SLA breach risk at ticket creation

POST
/churn/signal

Extract churn signal from thread history

GET
/metrics

Live system health and routing statistics

GET
/health

Health check for deployment pipelines