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Protégé

Protégé provides AI-ready real-world data and expertise for use across the AI development lifecycle.

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Overview

Protégé is an AI Data Platform that connects AI model builders with proprietary, real-world datasets across healthcare, video, audio, speech, and spatial/physical intelligence domains, with enterprise pricing tailored to engagement scope. It serves frontier AI labs, healthcare AI startups, and enterprise model builders who need high-quality, non-public training data with clear provenance and rights protections.

Founded as Protege Health, Inc. and headquartered in New York City, the platform raised a $25 million Series A in February 2026 followed by a $30 million Series A extension led by Andreessen Horowitz (a16z), bringing total Series A funding to $55 million. Protégé operates as a two-sided marketplace: AI model builders gain streamlined access to curated datasets for pre-training, post-training, fine-tuning, and evaluation, while data providers (hospitals, media companies, motion capture studios, audio archives) monetize existing data assets while maintaining ownership rights and provenance tracking. The platform recently launched dedicated Healthcare AI Evaluation Datasets and Benchmarks, and powers Vals AI's clinical documentation and medical billing benchmarks.

Based on our analysis of 870+ AI tools, Protégé occupies a distinct niche compared to general-purpose data labeling platforms like Scale AI or Labelbox—it focuses on sourcing genuinely proprietary, non-public real-world data rather than annotating publicly available content. Customer testimonials position the company as a hands-on data partner; Mahesh Ranganath of Siemens Healthineers describes Protégé as "an internal partner...helping us dig into exactly what data we need for the specific problem we're trying to solve, rather than simply being a data catalog." This consultative approach distinguishes it from self-serve data marketplaces and makes it particularly suited to teams building domain-specialized models where data scarcity is the critical bottleneck.

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Key Features

Full AI Lifecycle Data Coverage+

Protégé organizes its offering around four distinct stages of model development: pre-training (massive, diverse real-world datasets), post-training (narrower datasets for supervised training and human feedback), fine-tuning (curated domain-specific datasets), and evaluation (uncontaminated benchmark data). This staged approach lets teams source data appropriate to their current development phase rather than forcing a one-size-fits-all dataset purchase.

Healthcare AI Evaluation Datasets and Benchmarks+

Launched in early 2026, Protégé's healthcare evaluation product provides benchmark-specific real-world data that does not overlap with common training corpora and reflects the full multimodal patient journey. The same data infrastructure powers Vals AI's healthcare benchmarks for clinical documentation (turning encounters into usable clinical notes) and medical coding (translating care into billable codes), giving the offering external validation.

Two-Sided Data Marketplace with Rights Protection+

Beyond serving model builders, Protégé operates a 'For Data Providers' program that lets hospitals, studios, and enterprises monetize existing data assets. The platform emphasizes maintaining clear rights protections and provenance throughout each exchange, which is critical for regulated verticals like healthcare and for content owners concerned about downstream redistribution.

Multi-Modal Domain Specialization+

Rather than positioning as a general data vendor, Protégé organizes its catalog around five distinct domains: healthcare, video, audio & speech, spatial & physical intelligence, and other domains. Each domain has dedicated landing pages and presumably specialized expertise, allowing teams to engage with subject-matter experts rather than generalists when sourcing data.

Consultative Sourcing Model+

Protégé positions itself as an 'internal partner' rather than a self-serve data catalog, with named customers like Siemens Healthineers describing the team as helping dig into exactly what data is needed for specific problems. This high-touch model contrasts with marketplace-style data vendors and makes Protégé particularly useful when the right dataset doesn't yet exist off the shelf and must be assembled from provider relationships.

Pricing Plans

Enterprise

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  • ✓Custom data sourcing across healthcare, video, audio, speech, and spatial intelligence domains
  • ✓Full AI lifecycle coverage: pre-training, post-training, fine-tuning, and evaluation datasets
  • ✓Consultative engagement with dedicated data sourcing team
  • ✓Data provenance tracking and rights protection
  • ✓Access to Healthcare AI Evaluation Datasets and Benchmarks
  • ✓Two-sided marketplace participation for data providers
  • ✓Pricing tailored to data volume, modality, exclusivity, and engagement scope
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Best Use Cases

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Healthcare AI startups building clinical documentation, medical coding, or diagnostic models that require multimodal patient journey data sourced from real provider relationships

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Frontier AI labs running large-scale pre-training and seeking massive, diverse real-world datasets that go beyond publicly scraped web content

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Model evaluation teams needing uncontaminated benchmark data — datasets guaranteed not to overlap with training corpora — to honestly measure capability

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Computer vision teams working on spatial and physical intelligence models requiring motion capture, embodied data, or scarce real-world video footage

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Audio and speech model developers building voice agents or transcription systems that need licensed, high-quality voice data with clear provenance

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Hospitals, media companies, and content owners looking to monetize proprietary archives by licensing them to AI builders while preserving usage rights

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Protégé doesn't handle well:

  • ⚠No public pricing or self-serve onboarding means every engagement requires a sales conversation and likely a multi-week procurement cycle
  • ⚠Targeted at enterprise buyers — solo developers, hobbyists, and academic researchers without institutional budgets are effectively excluded
  • ⚠Dataset coverage depth varies significantly by vertical, with healthcare being the most developed and other domains still scaling
  • ⚠As a Series A company founded recently, Protégé has a shorter track record than incumbent data vendors and limited published case studies
  • ⚠Marketing site provides minimal technical detail on integration patterns, data delivery formats, API access, or evaluation tooling specifics

Pros & Cons

✓ Pros

  • ✓Backed by $55M in Series A funding (including $30M extension led by a16z) signaling strong investor confidence and runway
  • ✓Trusted by enterprise customers including Siemens Healthineers, validated by named testimonials from medical imaging leadership
  • ✓Powers third-party benchmarks including Vals AI healthcare evaluations for clinical documentation and medical coding
  • ✓Covers four distinct AI lifecycle stages (pre-training, post-training, fine-tuning, evaluation) rather than focusing on just one
  • ✓Strong focus on uncontaminated evaluation data — datasets explicitly designed not to overlap with training data
  • ✓Specializes in non-public proprietary data, addressing the actual bottleneck for frontier model improvements

✗ Cons

  • ✗Enterprise-only pricing with no transparent tiers, making it inaccessible to indie developers or small startups
  • ✗No self-serve data catalog — every engagement appears to require a sales conversation and custom data sourcing
  • ✗Domain coverage is broad but uneven; healthcare appears far more mature than other verticals like spatial/physical intelligence
  • ✗Relatively young company (Series A stage) with shorter operating history than incumbent data platforms like Scale AI
  • ✗Limited public documentation about technical integration, dataset formats, or API access on the marketing site

Frequently Asked Questions

What types of data does Protégé provide?+

Protégé sources real-world, proprietary data across five primary domains: healthcare (including multimodal patient journey data, clinical documentation, and medical imaging), video, audio and speech, spatial and physical intelligence (including motion capture), and other industry-specific verticals. The platform supports all four stages of the AI development lifecycle, from massive diverse pre-training datasets to narrowly curated fine-tuning data and uncontaminated benchmark datasets. Unlike public scrape-based corpora, Protégé focuses specifically on private and proprietary data that is not otherwise available.

How much does Protégé cost?+

Protégé uses enterprise pricing that is not published on its website, meaning all engagements require direct contact with their sales and partnerships team. Pricing is presumably tailored to the volume, modality, and exclusivity of the data being licensed, as well as the scope of the consultative work needed to source and prepare it. This model is consistent with other premium AI data platforms targeting frontier labs and enterprise customers, though it makes the platform inaccessible to smaller teams and individual researchers. Prospective buyers should expect a custom quote process rather than a public pricing page.

Who founded Protégé and how is it funded?+

Protégé operates under the corporate name Protege Health, Inc. and is headquartered at 169 Madison Ave, New York, NY. The company announced a $25 million Series A in February 2026 to expand its AI training data platform, followed by a $30 million Series A extension led by Andreessen Horowitz (a16z), bringing total Series A funding to approximately $55 million. The extension was driven by rapid adoption across healthcare, media, audio, motion capture, and other verticals as AI companies increasingly need high-quality, non-public data.

How does Protégé differ from Scale AI or other data labeling platforms?+

Based on our analysis of 870+ AI tools, Protégé differs from labeling-first platforms like Scale AI, Labelbox, and SuperAnnotate by focusing on data sourcing rather than annotation of existing data. Its core value proposition is connecting model builders with genuinely proprietary, non-public data held by hospitals, studios, and enterprises, with rights and provenance protections built in. Customer testimonials describe Protégé as a hands-on internal partner that helps identify the right data for specific problems, rather than a self-serve data catalog. This makes it more comparable to a specialized data brokerage than to a labeling tools vendor.

Can data providers monetize their datasets through Protégé?+

Yes — Protégé runs a dedicated 'For Data Providers' program that allows organizations holding proprietary datasets or content to generate revenue by licensing that data to AI builders. The platform emphasizes maintaining clear rights protections and provenance tracking throughout the exchange, which is particularly important for regulated domains like healthcare. Data providers can participate across the same five domains the platform serves: healthcare, video, audio and speech, spatial and physical intelligence, and other domains. This two-sided marketplace model is one of the platform's distinguishing features compared to pure-buy-side data vendors.
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What's New in 2026

In January 2026, Protégé launched its Evaluation Datasets and Benchmarks product line for Healthcare AI, providing benchmark-specific real-world data designed not to overlap with training data. In February 2026, the company closed a $25 million Series A to expand its training data platform, followed shortly by a $30 million Series A extension led by Andreessen Horowitz (a16z). Protégé-prepared data also began powering new Vals AI healthcare benchmarks for clinical documentation and medical billing in late 2025.

Alternatives to Protégé

Scale AI

Testing & Quality

Scale AI provides a data-centric infrastructure platform that accelerates AI development by combining human-in-the-loop data labeling with advanced automation. The platform supports the full AI data lifecycle—from annotation and curation to RLHF (Reinforcement Learning with Human Feedback) and model evaluation—serving enterprise customers including Meta, Microsoft, OpenAI, Toyota, and the U.S. Department of Defense. Scale's platform integrates with major ML frameworks and cloud providers (AWS, GCP, Azure), offers programmatic APIs for pipeline automation, and provides specialized workflows for computer vision, NLP, sensor fusion, and generative AI fine-tuning. Unlike competitors such as Labelbox or Snorkel AI, Scale differentiates through its managed workforce of over 240,000 contractors combined with proprietary quality-assurance algorithms, enabling high-throughput labeling at enterprise scale with configurable accuracy guarantees.

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