
Founded by the team that created the Uber Michelangelo platform, Tecton provides an enterprise-ready AI data platform to help companies activate their data for AI applications. AI creates new opportunities to generate more value than ever before from data. Companies can now build AI-driven applications to automate decisions at machine speed, deliver magical customer experiences, and re-invent business processes. But AI models will only ever be as good as the data that is fed to them. Today, it’s incredibly hard to build and manage AI data. Most companies don’t have access to the advanced AI data infrastructure that is used by the Internet giants. So AI teams spend the majority of their time building bespoke data pipelines, and most models never make it to production. We believe that companies need a new kind of data platform built for the unique requirements of AI. Tecton enables AI teams to easily and reliably compute, manage, and retrieve data for both generative AI and predictive ML applications. Our platform delivers features, embeddings, and prompts as rich, unified context, abstracting away the complex engineering involved in data preparation for AI. With Tecton, companies can: 1. Productionize context faster, getting new models to production 80% faster 2. Build more accurate models through rapid data experimentation and 100% accurate context serving 3. Drive down production costs by turning expensive workloads into highly efficient data services We believe that by getting the data layer for AI right, companies can get better models to production faster, driving real business outcomes. Tecton enables organizations to harness the full potential of their data, creating AI applications that are contextually aware and truly intelligent.

Founded by the team that created the Uber Michelangelo platform, Tecton provides an enterprise-ready AI data platform to help companies activate their data for AI applications. AI creates new opportunities to generate more value than ever before from data. Companies can now build AI-driven applications to automate decisions at machine speed, deliver magical customer experiences, and re-invent business processes. But AI models will only ever be as good as the data that is fed to them. Today, it’s incredibly hard to build and manage AI data. Most companies don’t have access to the advanced AI data infrastructure that is used by the Internet giants. So AI teams spend the majority of their time building bespoke data pipelines, and most models never make it to production. We believe that companies need a new kind of data platform built for the unique requirements of AI. Tecton enables AI teams to easily and reliably compute, manage, and retrieve data for both generative AI and predictive ML applications. Our platform delivers features, embeddings, and prompts as rich, unified context, abstracting away the complex engineering involved in data preparation for AI. With Tecton, companies can: 1. Productionize context faster, getting new models to production 80% faster 2. Build more accurate models through rapid data experimentation and 100% accurate context serving 3. Drive down production costs by turning expensive workloads into highly efficient data services We believe that by getting the data layer for AI right, companies can get better models to production faster, driving real business outcomes. Tecton enables organizations to harness the full potential of their data, creating AI applications that are contextually aware and truly intelligent.
Product: Enterprise feature platform / feature store for production ML (offline training + online serving)
Founding: Founded in 2019 by former Uber Michelangelo engineers (Mike Del Balso, Kevin Stumpf, Jeremy Hermann)
Funding: Raised $160,000,000 total (Series C $100M announced July 12, 2022)
Integrations: Integrates with Databricks, Snowflake, and Google BigQuery
Employees: ~180
| Company |
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Operationalizing ML features and the data layer for AI—making it easier to compute, manage, and retrieve production-ready data/context for models.
2019
Software Development
20000000
Company stated combined seed+Series A funding was reported at $25M at the time.
35000000
100000000
Series C brought reported total raised to $160M.
“Early/co-leads include Andreessen Horowitz and Sequoia Capital; Series C led by Kleiner Perkins with strategic participation from Snowflake and Databricks and additional investors including Tiger Global and Bain Capital Ventures.”