An AI infrastructure company
High-fidelity training data for the enterprise.
BeatpulseLabs creates custom multimedia AI training datasets for enterprise clients. Our platform accumulates and harnesses human intelligence, judgement and taste, converting it into actionable signals for the machine layer.
§ 01 — About
The way AI is trained is changing.
§ 01.1 — Origin
Raison d'Être
We created BeatpulseLabs because we believe the real value of AI will be unlocked inside the individual enterprises.
This next wave of Enterprise AI deployment will not rely on public datasets labelled by generalists.
Safe enterprise AI adoption requires a different approach to training data. Datasets need to be purpose-built and high-fidelity. Custom annotation schemas and continuous annotator feedback loops are no longer optional.
§ 01.2 — Today's AI Data Infrastructure
The Problem
Data foundries today rely on rigid annotation schemas and employ casual, often mid-skilled annotators and fragmented freelance operators. They rely on fragmented freelance pools and casual gig workers. No structured teams. No annotator feedback loops. No community. Annotation is treated as a temporary task, not a career. One-size-fits-all data extraction.
The result is generic datasets with high drift, unsuitable for production-grade enterprise AI.
This business model was well suited for 2021-2025 AI training, but it cannot produce the minimal data drift required for safe enterprise deployment.
§ 01.3 — Tomorrow's AI Data Infrastructure
Solution
BeatpulseLabs is set to close that gap. We develop custom annotation schemas for each client, often at the product-specific level. We partner with full-time domain specialists that we train and manage in structured teams.
We invest in our community, our culture and our tooling. All of our annotators are classically trained in their fields and they work with us because the work is meaningful and the environment is built for them.
The result is a different kind of training data. High fidelity. Low drift. Built for the next wave of AI enterprise deployment.
§ 02 — OUR PARTNERS
Trusted by leading AI firms
& disruptors worldwide.
§ 03.1 — Product
Intelligence Extraction
We translate human domain expertise, taste and judgment to the machine layer. BeatpulseLabs leverages our internal full-time specialist teams and purpose-built dataset creation software to produce training data for the Enterprise.
We keep the full process in-house by recruiting some of the world's leading experts, passionate about pushing the boundaries of AI in their field. We win on scaling performance and high dataset fidelity.
Our team proactively identify edge cases that could "break" current models and create training data to address them. Feedback is built directly into the product, improving annotation speed, usability, quality and overall output.
Feedback Loops
Most annotation is a one-way street. Data goes in, labels come out. Beatpulse is built differently. Our Subject Matter Experts are trained to think like product teams. They proactively identify edge cases, flag ambiguity and surface the inputs that current models consistently get wrong. That intelligence feeds directly back into the annotation process. The dataset gets sharper with every cycle. The model learns faster.
Custom Annotation:
No two enterprises see the world the same way. Generic annotation schemas produce generic intelligence. That is not good enough for safe, production-grade AI. Beatpulse builds and uses annotation environments and pipelines that are custom-designed for each client, each product and each use case. The schema encodes how your organisation thinks about its own data. Over time this crystallises institutional knowledge.
01.
In-house specialist teams
We keep the full process in-house by recruiting some of the world's leading experts, passionate about pushing the boundaries of AI in their field.
02.
Custom annotation schema
Generic schemas produce generic intelligence. Beatpulse builds annotation environments designed for each client, each product and each use case.
03.
Feedback loops
Subject matter experts proactively identify edge cases, flag ambiguity and surface the inputs that current models consistently get wrong. The dataset gets sharper with every cycle.
04.
Scaling SME performance
We win on scaling subject matter expert performance and high dataset fidelity. Not on headcount.
§ 03.2 — Product
Proprietary Datasets
Speech, video, music and radar. Pre-processed to client specifications,
structured with consistent metadata, ready to plug directly into training pipelines and fully IP-compliant.
We own and license one of the largest private multimedia catalogues built specifically for AI training. Speech, video, music and radar. Pre-processed to client specifications, structured with consistent metadata, ready to plug directly into training pipelines and fully IP-compliant.
§ 04 — Domains
Four Domaines: covered fully End-to-End
Each domain is covered by both products: Custom annotation (Intelligence Extraction) and Proprietary Datasets. We are laser focussed on delivering highly customised training datasets, fully in-house.
§ 05 — Scale
One of the largest private multimodal catalogues built specifically for AI training.
Speech, video, music and radar. Pre-processed to client specifications, structured with consistent metadata and fully IP-compliant.
1M+
Speech hours, 25 languages
1M+
Video hours, broadcast grade
800K+
Music tracks, stems, MIDI
10TB+
Radar live hotspot readings
Fully licensed
Ethically sourced and legally cleared for AI training. No scraping. No grey areas.
Not on the internet
None of our data is on the open web. Models trained on it learn something genuinely new.
Plug and play
Pre-processed to client specs. Consistent metadata. Drops into modern training pipelines.