Upriver

Upriver is an AI-native data engineering platform designed to automate and manage the complex data layer that enterprises rely on for their AI initiatives. The company was founded in 2023 by Ido Bronstein (CEO) and Omri Lifshitz (CTO), both graduates of the prestigious Talpiot military program with master's degrees in artificial intelligence. Their shared experience building large-scale intelligence systems for over a decade in elite Israeli defense units, including Unit 8200, exposed them to the persistent challenges of data engineering, which they initially solved by building internal tools. This experience led them to the realization that many data teams face the same operational burdens, inspiring the creation of Upriver.

The company operates on a B2B business model, targeting enterprise data teams. In June 2026, Upriver announced it had raised $14 million in a seed funding round led by Valley Capital Partners and Hetz Ventures, with participation from notable angel investors such as the founders of Cyera and the creator of New Relic. This funding is intended to expand its engineering and go-to-market teams. Upriver already serves clients like Unity and DMGT and has established partnerships with major data platforms, including Databricks and Snowflake.

The core of Upriver's service is an AI agent that connects directly to an organization's full data stack, including warehouses, orchestrators, and code. It builds a 'Living Map,' a continuously updated, cross-stack understanding of the entire data environment. This map allows the AI agent to perform a range of data engineering tasks autonomously, such as identifying and resolving incidents, building and maintaining data pipelines, accelerating system migrations, and answering queries about data lineage and metrics. The platform is designed to handle the repetitive, time-consuming work that often bogs down data teams, allowing engineers to focus on higher-value activities. Upriver's system provides validated fixes and reports, ensuring that engineers remain in control by reviewing and approving all changes before they are implemented.

Keywords: AI data engineering, automated data pipelines, data stack automation, data observability, incident resolution, data infrastructure management, AI agent, data operations, enterprise data, data reliability, pipeline maintenance, data lineage, Databricks, Snowflake, cross-stack data platform, AI-native data platform, data quality, data validation, data migration, data environment mapping, enterprise AI, data layer automation