Founded by Bobby Samuels, Protégé has raised $25 million in Series A funding to accelerate its mission of becoming the trusted infrastructure layer for AI training data. The round was led by Footwork, with participation from CRV, Bloomberg Beta, Flex Capital, Shaper Capital, and Liquid 2 Ventures, positioning the company as a critical player in the rapidly growing AI data ecosystem.

Solving the Hidden Bottleneck in AI Development

Over the last 18 months, the AI landscape has been dominated by advances in model architectures and GPU performance. But behind the scenes, many companies face a more fundamental challenge: finding, verifying, and managing high-quality training datasets. Most organizations rely on ad hoc scraping, fragmented repositories, or informal partnerships, which leads to biased models, inconsistent results, and longer development timelines.

Protégé is tackling that head-on with a secure, scalable data layer that allows organizations to discover, share, and evaluate AI training data across multiple domains. By combining robust data provenance tools with customizable access controls, the platform ensures that models are trained on verified, trustworthy sources rather than generic or untraceable inputs.

How the Platform Works

At its core, Protégé provides three integrated capabilities:

• Data Discovery - A structured marketplace where organizations can search and access relevant training datasets.
  
• Data Evaluation - Built-in tools to assess the provenance, bias risk, and fit-for-purpose quality of any dataset.
  
• Data Collaboration - A secure permissions framework that allows teams to share internally or with external partners without losing ownership.
  

These features not only save time, but also help companies comply with emerging AI governance and regulatory standards.

Why Investors Are Betting on Data Infrastructure

While many AI startups are focused on new generative applications or fine-tuning niche models, Protégé is building mission-critical infrastructure. The company’s approach is particularly attractive to investors because it sits at the foundation of every AI workflow. Regardless of industry or use case, accurate data curation is a prerequisite for effective model performance.

In announcing the raise, Bobby Samuels emphasized that the company’s long-term vision is to make trusted training data as accessible and auditable as public cloud compute resources are today. That value proposition resonated strongly with investors who view data infrastructure as the next layer of defensible value in the AI stack.

Here’s where the insight for other founders becomes impossible to ignore: Protégé didn’t chase the hottest segment of the AI hype cycle. Instead, it positioned itself at the necessary layer beneath the noise. That strategic decision reduced direct competition, made the product indispensable, and pulled the company into conversations with enterprise buyers who increasingly view training data quality as an operational risk. The lesson is clear - in crowded technological shifts, the companies that win often do not build the most visible product, they build the keystone that makes everyone else’s products viable. By relentlessly focusing on the unglamorous but unavoidable data layer, Protégé turned a background problem into the core of its value proposition and, in the process, made itself invaluable to the entire stack.

Roadmap, Expansion Plans, and Go-To-Market Strategy

Protégé plans to use the new capital to grow its engineering and data science teams, deepen integrations with existing enterprise data platforms, and expand its library of verified datasets across healthcare, finance, geospatial, and synthetic media.

Additionally, the company will launch an enterprise-focused onboarding program designed to help large organizations migrate legacy datasets into Protégé’s structured format. This will allow customers not only to use the platform for new AI projects, but also to clean and re-use historic data assets that are currently fragmented across internal repositories.

Shaping the Future of AI Governance

As global regulators introduce new AI transparency and provenance requirements, Protégé is well positioned to support compliance efforts. The platform’s audit-ready lineage tools allow organizations to clearly trace which data sets were used in which models - a feature expected to become critical in highly regulated sectors such as healthcare, finance, and public services.

Analysts believe that data infrastructure and provenance tools will become as essential to AI operations as DevOps pipelines are to software engineering today. Protégé’s traction with both investors and enterprise customers signals that the market is already starting to move in that direction.

Looking Ahead

Over the next 12 months, Protégé will:

• Expand its library of domain-specific datasets
  
• Deepen integrations with leading MLOps platforms
  
• Launch enterprise onboarding and governance tooling
  
• Continue building the underlying marketplace infrastructure to support data sharing at scale
  

With a clear vision and a growing customer base, the company is poised to become the de facto data layer behind tomorrow’s AI systems.