$7.5M+ · AI + Data Management

What they're building

LakeFusion is not a new data product bolted onto Databricks. It is MDM rebuilt from scratch inside the lakehouse so data never has to leave. Enterprises point the platform at the tables they want to clean, and LakeFusion runs AI-driven entity resolution and deduplication to produce golden records: single, trusted, governed master records for customers, products, suppliers, patients, properties, or any other critical domain. The rules engine, the governance layer, the survivorship logic, and the real-time sync back to CRM and ERP all run natively inside Databricks, which means no ETL pipeline, no separate MDM infrastructure, and no data movement. Install it from the Azure or AWS Marketplace, point it at your tables, and go. The platform balances LLMs with a broader suite of matching and deduplication techniques to stay cost-effective at enterprise scale.

Why this matters

Enterprise AI is stalling not because models are bad but because the data underneath them is broken. A GPT-4-level model trained on a customer database with 40% duplicate records produces confident nonsense. MDM, the discipline of creating single authoritative records across systems, has been the unglamorous fix for this problem for thirty years. The legacy players, Informatica, Reltio, Semarchy, are architecturally designed for a pre-lakehouse world: expensive, slow to deploy, and built around moving data into a separate MDM system. That architecture made sense in 2005. It makes no sense in 2026, when most enterprise data already lives in Databricks. Silverton Partners GP Mike Dodd said it precisely in the announcement: "As enterprise AI moves into production, the bottleneck is no longer model capability, it's data fidelity." LakeFusion's moat is the Databricks-native architecture. No other MDM platform is built this way. The Databricks ISV Partnership and dual Marketplace listings validate the technical approach from the ecosystem itself.