Next-Generation Conversational Platform
SysTalk.Chat uses NLU, FAQ and FLOW core modules to form a dual-brain, single-process architecture. Paired with AI, it provides smart and user-friendly conversational services.
The NLU brain perceives needs and necessary information through intent judgment and entity recognition, then provides accurate information for FLOW to connect subsequent service systems and respond to customer needs. The lightweight FAQ consultation brain supports hierarchical Q&A, which allows low-cost rapid processing of general questions and answers.

Dual brain + single process architecture: core module operating mode

Dual brain + single process architecture: core module operating mode
Dual brain + single process architecture: core module operating mode
Flexible flow editor for connecting the engines into a complete dialog service
  • High DOF: Drag-and-drop GUI, design flow for contextual design
  • Node-based: Easy to check errors
  • Built-in social media node: Easy to connect to other communication channels
Personalized Customer Service
  • Combines Natural Language Understanding technology and machine learning to perform intent classification and entity recognition, allowing Chatbot to understand the conversation content and correctly grasp the user needs and information for subsequent corresponding service processes
Quickly and accurately answer fixed-response questions
  • The FAQ module adopts six algorithms in two stages: fast screening and then accurate review
  • Multi-question and multi-answer function support
  • Synonym and deactivation design to improve response rate

NLU Brain Engine

I want to pay my credit card bill!

Using the N-Gram model, each conversation is decomposed and vectorized to obtain feature value results. The feature value is used as a base value for vector space positioning.

Comparing vector values. In addition, user intent is initially translated into necessary additional information.

The pre-trained high-dimensional language model defines the position.

Double-checks classified results.

Speed up online scheduling with deployment services

NLU Two Main Tricks

Entity recognition

Rational left brain

Identify the intended meaning of user conversation content to identify special and meaningful information from their wording.

Intention classification

Perceptual right brain

Analyze needs and intentions of users from conversation content, classify language data, and find the correct direction to proceed.

Brain Engine - FAQ Two-step Comparsion

Fast & Lightweight scoring engine

Step #1

Massive data scoring

With Elastic search, FAQ can quickly compare massive data and remove low to medium correlations. Low computing power.

Questions received

Step #2

Specific data comparsion

6 different algorithms filter the data and give a precise scoring.

Goodbye traditional Rule-Based Chatbot

SysTalk.Chat's Intent-Based Chatbot is more accurate.
Features of FAQ

Create hierarchical conversations

Simulate human conversations to reduce conversation fragmentation.

The right answer to multiple questions

Finding the right answer, regardless of the way customers ask the question.

Mark synonyms and disable words

Enhanced synonymy database to improve response rate.

Flow: Dialogue Process Controller

improve service completion rate
Multi-round conversations

Advanced customer service through follow-up inquiries.

Reduce bounce rate

Meet the natural user behavior


Increase first-time resolution rate


Improve service efficiency


Scale your project scope

Cost Saving
Minimize your operating costs while providing 24/7 customer service.
Service Flexibility
Multi-channel interface, allowing users to retrieve information and services quickly.
Improved Customer Service
Understand customer intent and improve user experience.
Get a Quote Dedicated Team

Complete import of the eight major processes has a dedicated import service team which excels at assisting enterprises in analyzing and classifying a large number of language data, planning business processes, and building AI brains. The team is made up of project managers, language trainers, and process planning engineers. helps you complete the SysTalk.Chat project successfully. Solution

Case Study
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