Pulse, a five-person startup specializing in unstructured data preparation for machine learning, has successfully raised $3.9 million in a seed funding round, led by notable figures including former GitHub CEO Nat Friedman. The company provides businesses with a toolkit to convert raw data into usable formats for machine learning models, addressing the increasing need for custom AI copilots and agents. With backing from prominent investors like Y Combinator and Sequoia Scout, Pulse aims to ensure accuracy and reliability in AI applications, particularly for sectors like finance and healthcare, where data errors can be critical.
"Let's say you're a financial institution or a healthcare company. There is no room for an LLM to make something up or hallucinate a number or an error," said Sid Manchkanti, cofounder and CEO of Pulse.
Pulse addresses the growing demand for enterprises to build custom copilots, chatbots, and digital agents tailored to their internal data.
Pulse sells businesses a toolkit designed to convert raw, unstructured data into formats ready for use by machine learning models.
Training data is the raw material that enables large language models to learn the relationships between words and phrases and mimic human-like text.
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