BeatpulseLabs, a London-based AI data company transforming expert human
judgment into high-fidelity training datasets for advanced multimodal AI
models, has raised $1.8 million in pre-seed funding. The round was co-led by
Araya Ventures and Lighthouse Ventures, with participation from Alumni Ventures
and Avalancha Ventures.
The funding announcement comes as BeatpulseLabs reports 10x revenue
growth during the first half of 2026, reflecting increasing enterprise demand
for high-quality, purpose-built AI training data.
As enterprise adoption of multimodal AI accelerates, companies are facing
a growing challenge: while access to raw data is abundant, creating datasets
that accurately capture human expertise, context, and decision-making remains a
significant bottleneck. BeatpulseLabs is addressing this gap by helping
organisations transform domain-specific knowledge into production-ready
training data.
Founded by South AfricanJason Rieff and BulgarianNikolay Vitanov, BeatpulseLabs was created to
address a fundamental limitation in artificial intelligence. Many multimodal
models continue to be trained on poorly annotated or generic datasets, reducing
their ability to perform reliably in real-world environments where context and
nuanced human judgment matter.
According to Vitanov, enterprise AI often encounters challenges when moving from controlled testing environments into real-world operations. He said BeatpulseLabs addresses this by creating training data that reflects how individual businesses actually function:
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.
BeatpulseLabs offers two integrated services: dataset preparation and
dataset provision. The company transforms existing multimedia content libraries
into enterprise-grade AI training datasets by cleaning, structuring, labelling,
validating, enriching, and formatting raw speech, music, and video assets for
machine learning applications. It also provides ready-made and custom
rights-cleared datasets for organisations seeking high-quality training data
without relying solely on their own content archives.
These datasets are designed to support model training, fine-tuning,
reinforcement learning, and evaluation, enabling AI systems to operate with
greater accuracy, context awareness, and reliability.
Rieff emphasised that the capabilities of AI systems are largely
determined by the quality of their training data, noting that much of the data
currently used is broad, inconsistently organised, and inadequately annotated
for enterprise use cases.
We are building the missing data layer by transforming raw multimedia
content into structured, annotated, model-ready datasets that help AI systems
understand context, not just patterns. The traditional approach of applying
broad labels to large volumes of content is no longer sufficient for the next
generation of AI.
The funding will support BeatpulseLabs as it expands its platform and
customer base amid growing demand for high-quality, domain-specific AI training
data.