Recently, V Fund investee company "EMOTIBOT" has completed D1 financing round of nearly 100 million yuan,which was jointly invested by KYMCO Capital, Jiangsu Cultural Investment and JUNCI Investment.

V Fund is the investor of EMOTIBOT's C financing round. Since V Fund's investment, the company has completed several financing rounds such as Series C+ and Series D respectively, and continues to be recognized by head investment institutions.

EMOTIBOT

 Founded in 2015 by Jian Renxian, former Vice President of Microsoft (Asia) Internet Engineering Institute, EMOTIBOT has been working on natural language understanding for many years, with knowledge, interaction, emotion and machine learning as its core. Starting from the B-side, where the need is more urgent, EMOTIBOT systematically trains smarter digital employees by following the path of "general technology base layer → industry terminology and scenario know-how → autonomous operation and maintenance without code", which is similar to real-person staff. In a market that is now generally focused on replacing low-end labor, EMOTIBOT goes a step further by providing assisted intelligence for high-end white-collar workers and helping enterprises integrate and revitalize their knowledge assets through a shared brain of multi-scene and multi-module digital employees to achieve a full-scene intelligent transformation.

 As a head player focusing on the NLP track in China, Jian Renxian, founder and CEO of EMOTIBOT, said: "We have always believed that knowledge asset-based management will become the new generation of operating system for enterprises, and we also hope that every customer can have their own digital employees and AI assistants, and NLP technology will be a strong support to achieve the above vision. Under the path of continuous iteration of digital employees, as long as there is a continuous input of knowledge assets, robots can learn endlessly; as long as the needs are clearly sorted out, any language-related scenario in any industry can have its own 'EMOTIBOT digital employee'; and through cooperation with ecological partners, using NLP to empower RPA, By collaborating with ecological partners and using NLP to empower RPA, OCR, CRM and other technologies and vertical applications in various fields, multi-ten thousand scenarios will be created. In the future, EMOTIBOT will continue to promote various in-depth cooperation with strategic investors and other eco-partners to continuously broaden the boundaries of product applications."

EMOTIBOT has developed its own NLP technology to bridge short and long texts, combined with deep learning technology to build a composite AI general technology base layer with logic-based symbolic AI and data-based neural AI, building a strong technical barrier.

 NLP is the core technology that gives AI its cognitive intelligence, and the degree of generality of NLP technology determines the extent to which digital employees can be produced at scale. From a linguistic perspective, language is divided into long text and short text, which belong to two language systems: written text and conversational oral language, and their corresponding main business application scenarios are knowledge and interaction respectively. A good NLP technology should be able to handle both long and short texts, but players in the market currently only focus on one of the two, mainly because the technology relied on to achieve natural language understanding is still relatively traditional. Short text interaction vendors usually use open source algorithms, rules, keywords, standard Q&A, etc., and rely on relatively limited corpus annotation in fixed scenarios to achieve the effect of barely replacing humans, but cannot achieve more intelligent dialogue effects such as multi-round Q&A and intent recognition; while long text is not practical to commercialize with pure machine learning due to the long length of the document and the large knowledge level. The depth of the corresponding application vendors can usually only handle documents with relatively uniform formatting and corpus in specific business scenarios, and cannot parse complex sentences or perform automatic annotation, and is even more powerless for spoken interaction scenarios with low standardization such as inversion, omission and multiple rounds.

The lack of technical depth determines the low level of product versatility, and the switching of industries and scenarios will rely on customization, which will easily expose the lack of momentum in productization, engineering and scale implementation. Deeply aware of this, EMOTIBOT chose a different technology path from that of a purely long-text application developer or short-text intelligent interaction vendor, using generic and composite NLP technology to bridge knowledge and interaction and build a complete technology stack. On the one hand, we have hired professional linguists to annotate the corpus at the beginning of technology development, and have developed a variety of technologies to deal with complex, non-standard and difficult sentences, including person recognition, denotation disambiguation, ambiguity understanding, entity linking and recognition, semantic role analysis, dependent syntax analysis, sentence vector contextual denotation analysis, error-tolerant recognition, etc. We have accumulated more than 30 NLP capability models and over 5 million linguist annotated. On the other hand, it combines deep learning technology to build a composite AI with logic-based symbolic AI and data-based neural AI, fusing linguistics and neural networks to map knowledge, opening the black box of AI thinking while also providing technical support for model compression and light annotation volume at the same accuracy rate.

Through 100% self-research, EMOTIBOT has built a solid technical base with natural language processing, knowledge engineering, multimodal emotional computing, deep learning, intelligent voice technology and text data middleware to equip digital employees with the knowledge base, communication, interpersonal and continuous learning capabilities.

The funding will be used to promote standardized SaaS products, continue to consolidate the technical barriers and product advantages, and help more head and waist customers to achieve intelligent transformation of their enterprises.