Metadata for GenAI workflows
Deasy Labs provides metadata orchestration for AI workflows. Deasie's platform provides the best way for AI teams to create and embed high-quality, customized metadata into their AI workflows (e.g., RAG, Agentic frameworks). Our three founders (from Amazon, McKinsey/QuantumBlack & MIT) previously built an ML data governance tool from 0 to 1 within McKinsey, which we deployed with 11 Fortune 500 companies. We saw in early 2023 the ability to create high-quality metadata (without reliance on domain experts) would be a key factor in achieving the accuracy & speed in GenAI applications required for production. Our investors include General Catalyst, Y Combinator, RTP Global and world experts in enterprise data. Website: https://deasylabs.com
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Active Founders
Reece Griffiths, Founder
Co-founder of Deasie: Data governance for language models in the enterprise. Prior experience: QuantumBlack: Product Manager for AI for Data Governance product (1.5 yrs); McKinsey & Company: Management consultant (2 yrs); X26: Founder of X26 (global entrepreneurs community); University of Cambridge: Masters in information engineering. Personal blog: https://reecewgriffiths.com
Mikko Peiponen, Founder
Mikko is the co-founder of Deasie. He is a technologist with deep experience (5+ years) in data and machine learning space from McKinsey and QuantumBlack, where he implemented and deployed models at large enterprises. Prior to moving into the world of data governance for language models with Deasie, he was the Lead data scientist building AI data quality tool at QuantumBlack. Mikko holds a graduate degree from MIT.
Leonard Platzer, Founder
Co-founder of Deasie: Data governance for unstructured data. Prior experience: QuantumBlack: Software Engineer, Machine Learning at AI for Data Quality product; Previous Engineer at Amazon, Mercedes-Benz and ChatBot Startup (E-Bot 7); Bachelor from National University of Singapore and Graduate from UWC Mostar
Company Launches
tl;dr: Deasie ensures that only relevant, high-quality, and safe data is fed into language models.
Hi! We’re Reece, Leo, and Mikko and we’re excited to launch Deasie.
⚠️ Problem
For the first time, companies are turning towards their masses of unstructured data (e.g., documents, reports, emails) for a range of Generative AI use cases. Today, most companies are unable to answer the critical questions needed to ensure language models are trained and deployed reliably: Does this data contain sensitive information? Is this the most relevant data for this problem? Are there inconsistencies in the data that could skew my results?
💡 Solution
Deasie is a platform that provides automated checks for compliance (e.g., PII & proprietary data) and quality (e.g., irrelevant, untimely, or inconsistent information) of unstructured data, to reliably govern which data is used for which language model use case.
👨👦👦 The Team
Founders Leo, Mikko & Reece previously built McKinsey's award-winning product that used AI to enhance data quality in the enterprise, built ML applications across QuantumBlack, MIT, Amazon, and Mercedes, and have now set out to unlock the power of unstructured data for the upcoming wave on language model applications.
👋 Ask
Lots of companies must be delving into the world of LLMs. If you’ve encountered challenges identifying and using high-quality input data (e.g., data that is relevant, timely, accurate, and consistent), then we’d love to hear from you! Please reach out to us at leonard@deasie.com