AI Startup Hilbert Secures $28M Series A to Transform How Companies Act on Customer Data

Exclusive: AI startup Hilbert lands $28M Series A

Why it matters: Companies are pouring money into AI without always seeing returns, which Hilbert hopes to change by helping automate business decisions that drive growth. How it works: Hilbert's AI software connects data across teams to help companies make decisions from a single system. * Using deep learning, Hilbert offers a more complete picture of its customers' data. It then structures that data so AI models can read it and suggest actions users can take. * Hilbert's tools also show the dollar value of taking those actions. * Walmart is already using the platform to better understand its shoppers' needs and how to act on them, according to Hilbert. What they're saying: "It's all about you learning from your company deeply," founder Nazli Tan told Axios. * Large language models make it easier to analyze company data, but most businesses still lack systems to act on it, Tan argues. * Enterprises that use AI agents alone "won't be able to get a compounding, big metric shifting company growth," she added. Customers include Walmart, FreshDirect, Blank Street and Levain. * Pricing scales with company size and data volume. * Customer contracts range in size from hundreds of thousands to millions of dollars. The bottom line: Startups like Hilbert are betting the next phase of AI isn't just generating insights, but driving and executing core business decisions.

Hilbert nabs $28M to ease analytics projects for consumer-focused companies - SiliconANGLE

Hilbert nabs $28M to ease analytics projects for consumer-focused companies Hilbert AI Co., a provider of analytics software for business-to-consumer brands, today announced that it has closed a $28 million funding round led by Andreessen Horowitz. Companies gather data about shoppers' buying preferences to find ways of optimizing their sales and marketing efforts. The more customers a company has, the more information it can collect. Large B2C brands have upwards of millions of customers, which is reflected in the size and complexity of their marketing datasets. Analyzing such datasets can be technically challenging. Hilbert's platform uses artificial intelligence agents to simplify B2C brands' analytics projects. According to the company, its software can complete analytics tasks that usually take months in a few minutes. Users specify which tasks should be performed and how through a natural language interface. One reason B2C brands' customer data troves are difficult to analyze is that they often contain a wide variety of records. Those records range from simple purchase logs to attribution tables, complex spreadsheet field collections that track what ad led to which purchase. Furthermore, such records are accompanied by metadata such as schemas. A scheme is a file that describes how the contents of a database should be formatted. Hilbert's platform organizes brands' datasets into a form that can be analyzed by AI agents. According to the company, data points that relate to the same customer are grouped into a file called a Hilbert Label. It contains key information such as whether the customer is at risk of unsubscribing or might be open to upsell offers. Hilbert regularly updates the file as new datasets are ingested. "What used to be a six-month, multi-headcount project can now be deployed in weeks," Andreessen Horowitz investors Bryan Kim, James da Costa, and Andrew Chen wrote in a blog post. "And the right data foundation doesn't just save money on headcount. It makes every growth dollar smarter, every experiment more trustworthy." After a B2C company's data is ingested, Hilbert analyzes it to find notable business events. The platform can, for example, detect if the effectiveness of a certain advertising channel suddenly increases. The software not only notifies the marketing team but also shows the cause of the change. Detecting marketing inefficiencies is another task that Hilbert promises to ease. The platform spots ad formats and discounts that aren't driving demand. Furthermore, it doubles as a forecasting tool that can predict future events such as customer base growth.