London-based AI data company BeatpulseLabs has secured $1.8 million in pre-seed funding to accelerate the development of its enterprise-grade AI training data platform.
The round was led by Araya Ventures and Lighthouse Ventures, with participation from Alumni Ventures and Avalancha Ventures. The investment comes as the company reports 10x revenue growth during the first half of 2026, reflecting increasing demand for specialized training datasets capable of powering next-generation multimodal AI systems.
Addressing One Of AI’s Biggest Bottlenecks
As artificial intelligence rapidly evolves, many organizations are discovering that access to data is no longer the primary challenge. Instead, the ability to capture human expertise, contextual understanding, and real-world decision-making within training datasets has become a critical competitive advantage.
BeatpulseLabs is focused on solving this problem by transforming human judgment and domain expertise into structured, high-fidelity datasets that help AI systems perform reliably in complex environments.
Founded by Jason Rieff and Nikolay Vitanov, the company believes that many AI models underperform in production because they are trained on generic, poorly structured, or insufficiently contextualized data.
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Building Enterprise-Ready Training Data
The company operates across two core areas: dataset preparation and dataset provision.
Its platform converts raw multimedia assets—including speech, music, and video content—into enterprise-grade training datasets through data cleaning, enrichment, validation, annotation, and formatting processes.
In addition, BeatpulseLabs provides ready-made and custom datasets that are fully licensed and rights-cleared, allowing organizations to access high-quality training data without relying solely on their own archives.
The resulting datasets are designed for model training, fine-tuning, and reinforcement learning, helping organizations improve model accuracy, reduce hallucinations, and shorten development cycles.
Expanding Beyond Media Into Enterprise AI
While the company initially demonstrated its capabilities within demanding multimodal domains such as music, speech, and video, BeatpulseLabs sees significantly broader opportunities ahead.
Its technology can be applied across industries where AI systems must make reliable decisions in highly specialized environments, including robotics, industrial operations, enterprise software, and knowledge-intensive workflows.
The company combines proprietary workflow software, human-in-the-loop annotation processes, and expert subject-matter knowledge to create datasets tailored to specific business requirements.
Founder Perspective
Nikolay Vitanov, Co-Founder of BeatpulseLabs, said:
“Enterprise AI rarely fails during testing. It fails when it encounters the complexity of real-world operations. Our approach is built around capturing how businesses actually function and embedding that context directly into training data. Generic datasets are no longer sufficient for the next generation of AI systems.”
Jason Rieff, Co-Founder of BeatpulseLabs, added:
“AI models can only be as effective as the data they learn from. Too much of today’s training data is generic, shallowly labeled, and optimized for accessibility rather than performance. We are building the missing data infrastructure layer that enables AI systems to understand context, not just patterns.”
Investor Confidence In The Data Layer Opportunity
Investors view enterprise-grade training data as one of the most important infrastructure opportunities in the AI ecosystem.
Mitul Ruparelia, General Partner at Araya Ventures, highlighted BeatpulseLabs’ ability to combine subject-matter expertise, workflow intelligence, and human judgment into scalable training datasets that address a growing challenge for enterprise AI adoption.
The newly secured funding will support expansion into additional industry verticals, product development, and continued growth of the company’s data infrastructure platform.
About BeatpulseLabs
BeatpulseLabs is a London-based AI data infrastructure company focused on transforming human expertise, judgment, and domain knowledge into high-quality training datasets for advanced AI models. By combining proprietary software, expert contributors, and exclusive multimodal data sources, the company helps organizations build AI systems capable of operating effectively in complex real-world environments.
