{"id":9050,"date":"2022-02-02T18:11:07","date_gmt":"2022-02-02T18:11:07","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2022\/02\/02\/pecan-ai-nabs-66m-to-simplify-ai-development-in-the-enterprise\/"},"modified":"2022-02-02T18:11:07","modified_gmt":"2022-02-02T18:11:07","slug":"pecan-ai-nabs-66m-to-simplify-ai-development-in-the-enterprise","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2022\/02\/02\/pecan-ai-nabs-66m-to-simplify-ai-development-in-the-enterprise\/","title":{"rendered":"Pecan AI nabs $66M to simplify AI development in the enterprise"},"content":{"rendered":"

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Artificial intelligence startup Pecan AI Ltd. today <\/span>announced<\/span><\/a> that it has closed a $66 million Series C funding round led by Insight Partners.<\/span><\/p>\n

S-Capital, GGV, Dell Technologies Inc.\u2019s venture capital arm, Mindset Ventures and Vintage Investment Partners participated in the round as well. Pecan AI\u2019s total outside funding now stands at $117.5 million.<\/span><\/p>\n

Israel-based Pecan AI provides a platform that enables companies to build AI models for tasks such as predicting customer demand and finding opportunities to reduce operational costs. Historically, building a custom neural network required specialized technical know-how, as well as a considerable amount of time and effort. Pecan AI says that its platform allows companies to deploy AI models in weeks without the need for specialized machine learning expertise.<\/span><\/p>\n

The most time-consuming aspect of machine learning projects is often not the AI development process, but rather the task of assembling a training dataset. A training dataset is a collection of records similar to the information that a neural network will be expected to process in production. The records are used to increase the neural network\u2019s accuracy before it\u2019s deployed.<\/span><\/p>\n

A company building an AI model to predict store sales, for example, might use a collection of historical store sales logs as its training dataset. Because business data is often scattered across multiple business units, assembling a training dataset may require collecting information from several different systems. Pecan AI\u2019s platform simplifies the task with connectors that can automatically retrieve information from a company\u2019s systems of record.<\/span><\/p>\n

The next step in machine learning projects is turning the collected training data into a form that the AI model can process. Pecan AI\u2019s platform promises to help with that task as well. The startup provides a drag and drop interface for performing data preparation tasks. The platform also promises to automate feature engineering, or the process of determining what factors an AI should consider when making a decision.\u00a0\u00a0<\/span><\/p>\n

When an AI analyzes a dataset to find a certain insight, such as how much revenue a store can be expected to generate in the next quarter, it more often than not doesn\u2019t analyze all the information in the dataset. Instead, neural networks prioritize the most relevant factors. An AI tasked with forecasting store sales might take into account how much revenue a retail location generated in the previous 30 days, but not what products customers purchased.<\/span><\/p>\n

Engineers usually have to manually specify what factors an AI should consider when making a decision. The task historically required\u00a0 specialized machine learning expertise. Pecan AI\u2019s platform, in contrast, allows users to perform feature engineering even if they don\u2019t have extensive familiarity with machine learning technologies.<\/span><\/p>\n

Once a machine learning project\u2019s training dataset is ready, Pecan AI automatically deploys several pre-packaged neural networks that could potentially be suitable for the customer\u2019s use case. The startup\u2019s platform runs a competition in which the neural networks are compared with one another. After the competition is complete, Pecan AI identifies the AI that proves most suitable for the project.<\/span><\/p>\n

Enabling business analysts without extensive AI expertise to create customized neural networks has several potential benefits for companies. One is that firms without an in-house machine learning team gain the ability to deploy more sophisticated AI software than they otherwise could.\u00a0<\/span><\/p>\n

A second benefit, which is also useful for companies that do have dedicated machine learning staff, is that automating AI engineering tasks requiring specialized know-how speeds up the development\u00a0 process. The result, in theory, is a faster return on investment. Pecan AI <\/span>says<\/span><\/a> that one e-commerce company achieved a revenue increase within 14 days of deploying a neural network created with its platform.\u00a0<\/span><\/p>\n

\u201cWe believe that any company should be able to deploy AI-based predictive analytics, even without data science resources on staff,\u201d said Pecan AI co-founder and Chief Executive Officer Zohar Bronfman. \u201cThis new funding will help us scale Pecan further to overcome the data science scarcity gap, enabling our customers to move beyond outdated data-mining techniques that offer little value in predicting future outcomes.\u201d<\/p>\n

Pecan AI says that its platform is used by several dozen companies, including Johnson & Johnson and other large enterprises. The startup <\/span>told<\/span><\/a> VentureBeat that its revenues have tripled over the past year but didn\u2019t share absolute numbers. As for the coming year, Pecan AI has plans to double its headcount.<\/span><\/p>\n

Image: Pecan AI<\/h5>\n
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