Tagged articles

Ticket Classification

4 articles · Page 1 of 1

Sep 24, 2026 · Artificial Intelligence

Designing Industrial-Grade Cross-Border E-Commerce AI Customer Service Agents: The Skills+Workflow Architecture

The article details the architecture of an industrial-grade AI customer service agent for cross-border e-commerce, using a Skills+Workflow dual-layer design to handle ticket classification, risk governance, and phased rollout, ensuring controllable automation with human-in-the-loop for high-risk actions.

AI AgentCross-border E-commerceCustomer Service Automation

0 likes · 21 min read

Designing Industrial-Grade Cross-Border E-Commerce AI Customer Service Agents: The Skills+Workflow Architecture

Su San Talks Tech

Sep 22, 2026 · Artificial Intelligence

Jev: The 200x Faster AI That Replaces Text Generation with Structured Decisions

Jev, a non-autoregressive 'System One' model from TypeSafe AI, replaces token-by-token text generation with parallel hidden-state scoring to deliver 70–500 ms latency and 40–400× cost savings for classification, routing, and scoring tasks, while returning calibrated probabilities that enable confidence-threshold automation in Java, Python, and JavaScript ecosystems.

AI AgentsJava SDKJev

0 likes · 21 min read

Jev: The 200x Faster AI That Replaces Text Generation with Structured Decisions

IT Services Circle

Sep 20, 2026 · Artificial Intelligence

Jev: TypeSafe's Decision Model for Software — Hands-on Demo & Critical Analysis

This article introduces Jev, TypeSafe AI's System One decision model for software, covering its three judgment types (Choice, Score, Noul), Python SDK integration with code examples, confidence threshold handling, performance claims (193x speed, 444x cost), and critical limitations including calibration vs. accuracy distinction and Chinese language considerations.

API integrationChoice Score NoulJev

0 likes · 12 min read

Jev: TypeSafe's Decision Model for Software — Hands-on Demo & Critical Analysis

Qunar Tech Salon

Aug 18, 2016 · Artificial Intelligence

Automatic Ticket Classification Using SVM and word2vec at Qunar

At Qunar, the data center algorithm team developed an automatic ticket classification system that combines Support Vector Machine with word2vec embeddings to handle high‑dimensional, low‑sample text data, achieving 89% accuracy and 80% recall while outlining the full machine‑learning pipeline from feature extraction to deployment.

QunarSVMText classification

0 likes · 7 min read

Automatic Ticket Classification Using SVM and word2vec at Qunar