Hybrid operations with 3-stage deployment eliminates risk
End users have their calls answered by a bot that has a library of responses to give based on the company’s dialog decision tree (DDT). Since agents don’t need to speak, they transition from speaking agents to silent agents. In stage I and II – training and verification – human agents and machine-learning bots operate in unison, while human agents have final control over actions taken. Then, in stage III, the bot operates independently.
Chatbot & Call center chatbot
AI CHAT BOT อัจฉริยะด้วยเทคโนโลยีปัญญาประดิษฐ์ขั้นสูง
AI Chat Bot คือโปรแกรมคอมพิวเตอร์ชนิดหนึ่งที่สามารถคุยโต้ตอบกับมนุษย์ได้ โดยสามารถนำมาประยุกต์เข้ากับหลากหลายธุรกิจเพื่อช่วยทำหน้าที่ในการให้ข้อมูล ข่าวสารแก่ลูกค้าและธุรกิจของท่าน
โดย Chat Bot แบ่งเป็น 3 ประเภทเช่น Text Chat Bot / Voice Chat Bot / 3D Chat Bot เรายังนำเทคโนโลยี ระบบ Chat Bot มาปรับใช้ให้เข้ากับ Smart Home ในปัจจุบันได้เป็นอย่างดี
AI Chat Bot อัจฉริยะด้วยเทคโนโลยีปัญญาประดิษฐ์ขั้นสูง สำหรับตอบโจทย์ลูกค้าองค์กร เช่น แชทบอทบน SOCIAL MEDIA เช่น LINE chatbot, Facebook chatbot … หรือ Call center chatbot การตั้งค่าง่าย มีหลากหลายฟังก์ชั่น มีระบบ REPORT ไม่พลาดทุกเหตุการณ์สำคัญ และเพิ่มโอกาสทางธุรกิจต่างๆได้มากขึ้น
ปรึกษาฟรี Click me!
1- Training
Bot learns by observing silent agents and end users. The bot does not make any decisions. Human agents control the bot’s actions by selecting responses from a menu.
2- Verification
Bot actions are verified by silent agents. The bot is making its own decisions, while a human agent reviews and approves (or changes) decisions before action is taken.
3- Independence
Bot makes decisions and takes actions . The bot is let loose in the wild and runs independently, making decisions and taking actions without human agent assistance.
Making human interaction with machines as intuitive as possible
Berry B Group’s automated conversational interfaces enable brilliant user experiences. With its next-generation statistics-based natural language processing (NLP) engines. Berry B Group’s breakthrough NLP technology and solid backing has set the company on the path to become the favored NLP engine for commercial applications.
Statistical NLP vs Rules NLP
With its next-generation statistical-based natural language processing (NLP) engines, Berry B Group sets a new standard for ease of use, quality of user experience and speed to impact for operators.
The Death of Rules-Based NLP
Traditional rules-based NLP fails for critical business
But the reality is rules-based NLP systems have severe limitations & are hard to implement. Effectively, all attempts to deploy rules-based NLP for critical business operations have failed due to issues of inaccuracy, poor user experience & prohibitive costs.
-Prohibitive costs
-Insufficient accuracy
-Poor user experience
The many limitations of rules-based NLP
Rules-based NLP systems learn in a manner comparable to how people learn a new language at school – with transcription, an alphabet, spelling and grammar rules. It’s a lot of work and a lengthy process with problems due to numerous layers of translation, data loss and biases.
-High risk of misunderstanding the meaning
-Transcription, dictionaries and grammar rules
-Numerous layers of translation with data loss and biases
Next-Generation Statistical NLP
Berry B Group offers a new approach to NLP that is 100 percent statistical We believe the most efficient way to learn – for machines and humans – is to make natural associations betweenstimulus and meaning, as opposed to applying complex human-made rulesets. The approach is to mimic nature as much as possible.
No complex human-made rulesets
100% statistical-based
100% true machine learning
Berry B Group’s NLP is self-learning and writes its own rulesets
Berry B Group-powered bots observe human agents and end users, learn at lightning speed, and build their own sophisticated rulesets. Machine-built rulesets can have near unlimited complexity and subtlety, resulting in extraordinary accuracy.
-No expensive dictionaries and no expensive consultants
-Pattern recognition technology is language agnostic
-Machine-built rulesets deliver breakthrough performance
Pattern-based learning instead of rules-based learning
When Berry B Group bots learn by observing agents and end users, it’s comparable to the way children learn their first language at home from family: with direct allocation of meaning to any given stimulus.
PIXEL – For still and video/moving images
Berry B Group PIXEL analyzes both still and video images and finds the most granular data to identify patterns and their associated meanings. One use case is for driverless cars, where gestures and facial expressions can be used to determine, for example, when to turn the heat on or the music off.
PIXEL bots learn when and why to do things, driven by patterns identified from sometimes very subtle visual cues.
We have an exciting pipeline of new products coming to market soon, including specialized NLP engines for still and video/moving images and for financial markets. These solutions utilize Berry B Group’s next-generation statistical NLP technology to deliver breakthrough results for broad marketplace use cases, identifying patterns and understanding best next-step actions to take.
