Hilbert Raises $28 Million to Turn Growth Data Into Automated Decisions

SAN FRANCISCO, Calif. - Hilbert has raised $28 million in Series A financing led by Andreessen Horowitz, marking a significant bet on the next generation of automated decision-making for the consumer sector. The San Francisco-based startup is building what it describes as an agentic analytics platform, moving beyond the traditional constraints of business intelligence to create a system that connects information across disparate corporate departments. The primary objective is to help growth teams identify and execute specific actions most likely to yield a measurable financial return, bridging the gap between raw data collection and profit-oriented execution.

The founding team, comprised of Nazli Tan, Ceyda E., Ozgur Akaoglu, and Cenk Batman, brings deep operational experience to the venture. The group previously built growth systems at the rapid-delivery company Getir, a high-velocity environment where the intersection of logistics, pricing, and customer demand required real-time recalibration. This background informed the central thesis of Hilbert: that for modern consumer businesses, the speed of data generation has outpaced the human ability to interpret dashboards and act upon them effectively. By codifying these complex workflows into an automated platform, the founders aim to standardize how companies process growth levers.

Hilbert’s software is designed to unify data silos that often plague large-scale consumer operations. In many organizations, marketing spend, dynamic pricing, merchandising inventories, and customer support metrics are treated as separate workstreams managed through independent tools. Hilbert integrates these datasets, providing a centralized perspective that allows for more holistic decision-making. The platform does more than just aggregate this data; it recommends or directly automates decisions while providing an estimated dollar impact for each potential action, allowing executives to weigh the financial opportunity costs of their strategies.

This functional focus distinguishes Hilbert from conventional business intelligence (BI) tools that have dominated the enterprise landscape for decades. Traditional BI platforms generally focus on organizing historical data into visual reports and dashboards, leaving the actual interpretation and subsequent task execution to human analysts. Industry observers note that while these tools provide visibility, they often lead to a 'dashboard fatigue' where insights do not necessarily translate into operational changes or revenue growth. Hilbert is instead attempting to create an active decision layer that sits on top of the data stack, shifting the paradigm from observation to intervention.

The company has already secured a roster of recognizable consumer brands for its early validation phase. Hilbert identifies Walmart, FreshDirect, Blank Street, and Levain Bakery among its current customer base. These partnerships span from massive traditional retailers to boutique bakeries and digital-native grocery services, suggesting that the problem of fragmented growth data is a universal pain point across various scales of the consumer industry. For these brands, the value proposition lies in the ability to move through the feedback loop of hypothesis, testing, and implementation at a pace that manual analysis cannot match.

The entry of Andreessen Horowitz into the Series A round underscores the broader venture capital interest in 'agentic' software—systems that can not only think but act with a degree of autonomy. While much of the recent focus in Silicon Valley has been on generative AI for content creation, a parallel movement is occurring in back-office operations where companies are seeking ways to turn predictive models into autonomous agents. The investment suggests a belief that the future of enterprise software lies in outcome-oriented tools that are measured by their ability to drive key performance indicators rather than their ease of use as a visualization tool.

However, the move toward automating high-stakes growth decisions is not without significant operational risk. By removing some of the manual friction between data and action, companies expose themselves to the potential for automated errors at scale. A model might identify a highly promising promotion or a lucrative customer segment but fail to account for external supply chain constraints, sensitive brand considerations, or rapidly shifting market conditions that are not yet reflected in the historical dataset. The difficulty in capturing 'common sense' business logic remains one of the primary hurdles for any platform aiming to automate corporate strategy.

To mitigate these risks, large enterprise customers will likely demand robust oversight mechanisms. As Hilbert matures, the necessity for approval controls, clear explainability of model logic, and sophisticated attribution modeling will become paramount. Stakeholders require transparency to understand why a specific pricing or marketing action was recommended, especially when those actions involve millions of dollars in potential revenue. Without a way to definitively prove that a recommended action caused a specific outcome, the system may struggle to gain the full trust of conservative finance and operations departments.

Hilbert plans to utilize the $28 million in new capital to aggressively expand its product capabilities and grow its core team. The funding arrives as the company enters an increasingly crowded market environment. Hilbert must compete for budget against established data warehouses, specialized marketing automation suites, and the internal analytics groups that many large retailers have spent years building. The competitive landscape is shifting as legacy software providers attempt to add their own layers of automation and predictive analytics to their existing product portfolios.

The startup’s central case to prospective customers rests on its ability to transcend 'vanity metrics' and attractive insights in favor of hard financial results. In an economic climate where consumer businesses are under pressure to optimize margins and improve retention, the promise of a tool that can be tested directly against revenue and profit is a compelling one. Analysts suggest that the success of Hilbert will rely on its ability to demonstrate a clear and recurring return on investment that justifies its place in an already expensive enterprise software stack.

Looking ahead, the industry will be watching to see how Hilbert handles the complexities of real-world integration across diverse retail environments. Each customer brings a unique data architecture and specific operational nuances that can be difficult for a standardized platform to ingest. If Hilbert can successfully automate decision-making across these varying contexts while maintaining high accuracy, it could set a new standard for how consumer companies manage growth. The ability to provide consistent measurement will be the essential yardstick by which the company’s long-term promise is ultimately judged.

As the Series A capital is deployed, Hilbert’s growth will serve as a bellwether for the adoption of automated agents in the corporate world. Whether businesses are truly ready to hand over the reins of growth strategy to algorithmic systems remains an open question. For now, Hilbert represents a bold attempt to transform the passive data lake into a proactive engine for commerce, signaling a future where the distance between seeing a market opportunity and capturing its value is reduced to a matter of code.