“Euler Cognitive Intelligence” completed tens of millions of PreA+ rounds of financing, Tianshi Innovation Investment

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Author｜Wu Sijin

Editor｜Wang Yutong

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36氪 was informed that the intelligent BI manufacturer “Euler Cognitive Intelligence” has completed the tens of millions of PreA+ round of financing exclusively invested by Tianshi Innovation Capital. This round of financing was served by Yiren Capital as the exclusive financial advisor, which is also its second completed this year. financing.

At the moment of digital transformation, the top priority of enterprises is to help employees build data thinking and form a corporate culture that uses data to assist decision-making. “Traditionally, enterprises solve the problem by setting up a data center. This kind of technical thinking makes front-line business personnel have to obtain professional data through IT personnel or data analysts, and does not really shorten the distance between business personnel and data. The development of intelligent BI is based on this.” Wang Xugang, founder and CEO of Euler Cognitive Intelligence, introduced to 36氪.

Different from the traditional BI based on the wide table model, which usually requires the business department to initiate a demand application to the data analysis department, intelligent BI realizes data preparation, visual exploration, and insight generation through a series of technologies such as NLP, ML, and enhanced analysis. , enhanced self-service analytics, and more. Front-line business personnel can generate relevant data analysis results only by entering keywords in the search box; it is easy to use, fast and efficient, and greatly reduces the dependence of enterprises on data analysts/IT personnel.

The Euler Cognitive Intelligence introduced in this article was established in 2019. It is an intelligent BI SaaS software. The core feature is that its data analysis is generated based on a purely self-developed graph computing engine. The graph computing engine is designed on the graph query system, which can simultaneously support efficient graph query and graph computing operations in a single system. For example, e-commerce platforms hope to be able to make personalized recommendations for users based on graph data (graph computing) in the case of querying users’ historical orders (graph query).

“With the rapid development of digitization, the diversity and relevance of data directly affect the value of data, and the graph structure model can well demonstrate the complex correlation between data and achieve reasonable predictions; in addition, the semantics of graphs Expression is also one of the key technologies to achieve the effect of “search as application”. Euler’s team and I not only have relevant invention patents and rich practical experience in “real-time graph computing”; the company’s self-developed graph computing engine does not need to be matched. Other components and databases have lower overall costs.” Wang Xugang said when introducing the reason for choosing graph computing technology as the underlying technology for data analysis.

In previous reports , 36氪 introduced Euler’s business model in detail. In the past 8 months, ORA has achieved a leap from the communication industry to the retail industry, and has accumulated nearly ten medium and large enterprise customers.

When talking about the reasons for choosing the retail industry, Wang Xugang said: “For data analysis products, the quality of data directly affects the accuracy of the results, and the data foundation of the retail industry is relatively complete, whether it is the amount of data or the variety of data. At present, the integrity and integrity are significantly better than other industries, which is why retail companies are most receptive to data-driven decision-making, and have the most urgent need for refined operational data.”

However, whether intelligent BI manufacturers can successfully take root in new industries depends not only on whether the products are easy to use, but also on whether they have built a complete industry knowledge map . Because for enterprises, if the data results generated by the search are only the information of the enterprise, it is only a tool, and it solves the problem of efficiency; and the construction of industry knowledge map caters to the operation of market research and analysis of enterprises. demand, can increase the added value of the tool.

Wang Xugang said that in building an industry knowledge map, Euler’s approach is to internalize industry brands, categories, products and contacts into an industry knowledge structure through interviews with industry experts, which is “out-of-the-box” for enterprises. . This also creates new opportunities for ORA to cooperate with retail companies mainly in physical stores. Due to the lack of online data, such companies need to rely on external industry data such as sales performance and user reputation to assist in optimizing their operation strategies.

For enterprises with complete online data, by correlating internal data such as master data, people and goods yard data, etc., to build enterprise-level and scene-level knowledge maps , front-line employees can search for data according to different needs and dimensions, such as sales are more concerned.” The performance and operation of “market” pay more attention to the data of “people”, while the production and research pay more attention to the trend of “goods”.

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Source: Euler Cognitive Intelligence

In addition to product functions, for enterprises, the accuracy of search results and the ease of use of operations may be the most important things for enterprises, which place high requirements on the closeness of correlation and particle density between data, but obviously not all The data of retail enterprises all meet this condition. To this end, Wang Xugang introduced:

First of all, in the case of different data quality, Euler uses technical means to maximize the accuracy of the analysis results. One is semantic analysis. Euler does basic semantic recognition and semantic entity relationship attribute analysis, and abstracts the relationship into a path from the starting point to the end point of the walk. The second is graph construction. Euler supports entity extraction and relation extraction for text data and small samples; at the same time, large model technology is used to transfer knowledge of the graph structure itself, which is convenient for cross-customer data migration in the same industry and between similar industries. knowledge transfer.

Secondly, focus on the balance between the accuracy and coverage of the results in terms of interactivity to ensure the user’s search experience and sense of gain.

Once again, Euler pays great attention to the ease of use of the product. For basic data analysts/IT personnel, they can quickly get started and experience the value of the product; for front-line business employees without basic knowledge, in addition to simple training , and also built-in common analysis boards and hot search words to help the other party establish usage habits and a sense of gain.

In the field of intelligent BI, Euler’s challenges mainly come from Baidu Intelligent Cloud Sugar BI and Microsoft Power BI represented by large manufacturers; MagicBI, which also benchmarks ThoughtSpot; and traditional BI manufacturers that are trying to expand into intelligent BI, such as Smart BI Software Smartbi, FanRuan, etc.

When mentioning the difference from benchmarking BI vendors and core competitors, Wang Xugang said: Technically, ThoughtSpot is a multi-dimensional index table, and its index does not contain attributes, while Euler adds attributes to the index. The storage structure of the two is relatively similar; the underlying technology of MagicBI is to convert natural language into SQL language on the basis of traditional management system database, and the calculation mode has not fundamentally changed. Euler’s graph data structure has changed the previous computing mode, and it is better in terms of flexibility of search and real-time calculation, and it is lower in the cost of importing data in the early stage of modeling and maintenance in the later stage.

In 2021, Euler’s revenue will reach tens of millions, and the revenue model is mainly based on the amount of data. According to Wang Xugang, this year’s revenue is expected to be 150% of last year’s. Up to now, Euler has accumulated dozens of large customers, and the renewal rate of customers in the original communication industry is over 90%. In terms of customer acquisition methods, there are two types of direct sales and channel sales.

According to reports, this round of funds is mainly used to continue to improve the capabilities of the graph computing engine, accelerate product iteration and further expand the market.

Investor’s View:

Zhou Guiliang, Founding Partner and CEO of Tianshi Innovation Capital, said: We are firmly optimistic about the technical precipitation and marketization capabilities of Euler’s founding team in the field of graph computing. Technically speaking, new technologies represented by graph computing have revolutionized the previous computing and data storage methods, and have natural advantages in dealing with complex relationships. The integration of new technologies such as AI and IoT has outstanding market performance. In the future, more and more enterprises will apply graph computing technology and graph database products in the production environment. The graph database will be more regarded as an independent infrastructure product, and users will pay more attention to the performance, cost, and ease of use of the database. Usability and ease of maintenance. Tianshi Innovation Capital focuses on investing in new consumer brands, new retail services, new technologies and supply chain innovation. Zhou Guiliang said that we will make full use of the investment team’s in-depth research in the field of consumer retail supply chain and the rich industrial resources accumulated in the future. Capital planning and linking industry upstream and downstream customers empower the Euler team, especially to support Euler’s expansion of related business scenarios in the consumer retail field, and help Euler’s business innovation in the retail field.

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