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The AI Data Team
Sherpa Labs is building an agentic data team that automates modeling, operations, and discovery via a swarm of agents. Their initial product is a data catalog that enables developers and AI agents to quickly locate the right data sources, effortlessly trace data lineage, and understand messy data lakes — similar to Glean but for enterprise data systems.
Sherpa Labs provides an AI-powered platform that acts as an assistant for modern data teams, aiming to automate ETL, modeling, and discovery through agent-based capabilities. It emphasizes natural language interaction, data dictionaries, lineage visualization, and AI-driven search across the data ecosystem.
A data workflow platform where an AI-powered assistant, Finn, helps navigate and manage data ecosystems via natural language queries. Features include AI-powered search across data infrastructure, auto-generation and enforcement of data dictionaries, data lineage visualization with impact analysis, and integration-friendly agents that connect with other tools and agents. The system aims to automate ETL, modeling, and discovery through agentic technology and extensible APIs to fit modern data teams' needs.
Who it’s for: Modern data teams and data-driven organizations seeking AI-assisted data management, discovery, and governance.
Product-focused with demo scheduling; mentions partnerships and enterprise-oriented features, ongoing hiring and growth signals implied by company growth and 2025 copyright.
Co-founder of Sherpa Labs: working on helping data engineers stay sane. Most recently I worked in Quant Research (Citadel, Two Sigma, etc...) working on statistical models for automated trading and before that worked on NLP research at Big Tech (Facebook, Microsoft). I studied Statistics (bachelor's) and Computer Science (master's) at Harvard.
Building the AI data team @ Sherpa Labs. Ex-quant researcher from Optiver, working on high frequency trading algorithms. Data scientist at FedEx and Tradeweb. Graduated from Harvey Mudd with a double BS in Math and CS.
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