AI startup BeatpulseLabs raises £1.3m led by Araya Ventures and Lighthouse Ventures
This article covers BeatpulseLabs, a London-based AI startup, which has raised £1.3m in a pre-seed funding round led by Araya Ventures and Lighthouse Ventures, with participation from Alumni Ventures and Avalancha Ventures. The funding backs a platform that converts expert human judgement into high-fidelity training datasets aimed at improving the reliability of multimodal AI models used by enterprises.
BeatpulseLabs, a London-based AI startup, has raised £1.3m in a pre-seed funding round led by Araya Ventures and Lighthouse Ventures, with participation from Alumni Ventures and Avalancha Ventures. The raise backs a platform that converts expert human judgement into high-fidelity training datasets aimed at improving the reliability of multimodal AI models — a practical bottleneck for enterprises deploying AI beyond controlled tests.
Why it matters
Multimodal models — those that combine speech, video and audio — are increasingly used in enterprise settings where errors have real costs. BeatpulseLabs says the problem is not a lack of raw data but the scarcity of context-rich, rights-cleared datasets that encode subject-matter judgement. The startup reported 10x revenue growth in the first half of 2026, which it points to as evidence of rising enterprise demand for bespoke, high-quality training data that reduces hallucinations and shortens training time.
Inside the product
BeatpulseLabs offers two core services. Dataset preparation transforms an organisation’s existing multimedia libraries into enterprise-grade training assets through cleaning, structuring, labelling, validating, enriching and formatting of speech, music and video. Dataset provision supplies ready-made or custom, rights-cleared datasets for teams that cannot or do not want to build datasets from their own archives.
The company combines licensed proprietary datasets with human-in-the-loop annotation and deep metadata enrichment so models can learn context as well as patterns. The press material highlights use cases in demanding multimodal domains such as music, video and speech, and suggests the same approach applies where margins for error are low — for example in robotics or complex knowledge work.
Who are the investors?
The round was led by Araya Ventures and Lighthouse Ventures, with participation from Alumni Ventures and Avalancha Ventures. BeatpulseLabs frames the raise as strategic capacity expansion rather than emergency capital, with funds earmarked for growth into new domains and to scale delivery for enterprise customers.
In the announcement, Mitul Ruparelia, General Partner at Araya Ventures, said:
BeatpulseLabs is tackling one of the most fundamental bottlenecks in Enterprise AI today: creating datasets beyond scale and general-purpose labelling, by embedding Subject Matter Expertise product-specific workflows, and high-fidelity human judgement directly into the data that powers Enterprise AI models.
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Founder perspective
In the announcement, Nikolay Vitanov, Co-founder at BeatpulseLabs, said:
Enterprise AI doesn't fail in testing. It fails when it meets the real world. BeatpulseLabs closes that gap by building training data around how each business actually operates. 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.
In the announcement, Jason Rieff, Co-founder, BeatpulseLabs, said:
AI models are only as capable as the data they are trained on. Today, too much training data is generic, messy, and shallowly labelled, chosen because it's easy to access rather than being fit for purpose. We're building the missing data layer: transforming raw multimedia content into structured, annotated, model-ready datasets that help AI systems understand context, not just patterns. The old approach of throwing broad labels onto available content is no longer enough for the next generation of AI.
The bigger picture
The raise and the reported revenue growth underline a broader shift: as enterprises move beyond proof-of-concept AI, the market is paying more attention to data quality and provenance. The deal reflects growing interest from AI investors in companies that provide foundational data infrastructure rather than model-only plays. For UK-based AI companies, better dataset tooling and rights management are likely to be a competitive advantage as regulators and customers demand more reliable, explainable systems.
BeatpulseLabs’ London base places it in a compact but active UK AI ecosystem that is increasingly focused on production-ready tooling and data infrastructure — areas likely to see continued investor interest across Europe as models are deployed at scale.
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