BeatpulseLabs Raises $1.8M Pre-seed To Scale AI Training Data Platform

The round was co-led by Araya Ventures and Lighthouse Ventures, with participation from Alumni Ventures and Avalancha Ventures.

The funding comes as the company reports 10x revenue growth in the first half of 2026, driven by rising enterprise demand for high-quality, purpose-built AI training data.

As multimodal AI adoption accelerates across enterprises, companies face a growing challenge: while data is widely available, building datasets that capture real human expertise, context, and decision-making remains difficult. BeatpulseLabs addresses this gap by transforming domain-specific knowledge into production-ready training data.

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Founded by Jason Rieff and Nikolay Vitanov, the com pany was built to overcome a key limitation in AI systems training data that is often poorly annotated, generic, or inconsistent, which reduces model performance in real-world environments.

BeatpulseLabs offers two core services: dataset preparation and dataset provision. It converts existing multimedia libraries into structured, enterprise-grade datasets by cleaning, labeling, validating, enriching, and formatting raw speech, music, and video data for machine learning use cases.

It also supplies ready-made and custom rights-cleared datasets for organisations that lack sufficient internal data.
These datasets support model training, fine-tuning, reinforcement learning, and evaluation, helping improve accuracy, context awareness, and reliability of AI systems.

The company highlights that AI performance is heavily dependent on training data quality, noting that much of the existing data is broad, inconsistently structured, and not suitable for enterprise-grade applications. The funding will help BeatpulseLabs expand its platform and customer base amid rising demand for specialised AI training data.

According to Vitanov enterprise AI often struggles when moving from controlled testing environments to real-world operations. He explained that BeatpulseLabs addresses this challenge by building training data that reflects how individual businesses actually operate, improving model performance in practical, real-world conditions.

We proved this approach in some of the most demanding multimodal domains such as music, video and speech. The same logic applies anywhere the margin for error is low, from robotics to knowledge work. Using generic training data is like letting a confident stranger make decisions for your business. We do not recommend it.

About BeatpulseLabs

BeatpulseLabs is the data layer for multimodal GenAI, creating IP-cleared, private, high-fidelity training datasets for video, speech, and music models. It transforms human intelligence, judgment, and taste into structured machine-learning signals, enabling enterprise clients to build more accurate, context-aware, and reliable generative AI systems.