LakeFusion, a Databricks-native master data management platform, raised $7.5 mn in Seed funding led by Silverton Partners, with participation from existing investor Carbide Ventures.
The company plans to use the funding to expand engineering operations and grow enterprise sales teams as demand rises for AI-ready data infrastructure across healthcare, manufacturing, and financial services.
Enterprises spent heavily on AI systems and cloud data platforms during the past two years. Many still struggle with the same issue underneath all of it: fragmented master data spread across CRM systems, ERP software, and operational databases.
The result usually looks ugly. Duplicate records pile up, customer hierarchies break, reporting slows down, and AI systems start producing unreliable outputs because underlying data lacks consistency.
According to Beinsure analysts, enterprise AI deployments increasingly stall at the data layer rather than the model layer.
Large organizations often discover their internal systems were never designed to support unified AI workflows across fragmented operational environments.
The company delivers AI-powered master data management directly inside the Databricks environment without forcing organizations to move data into separate infrastructure stacks or third-party MDM systems.
That architecture matters because enterprises already dealing with sprawling cloud environments want fewer duplicate pipelines, not more.
LakeFusion performs large-scale entity resolution and deduplication using AI-driven contextual identification models.
Organizations can create and maintain golden records inside the lakehouse environment, apply governance and survivorship rules, and synchronize master data across operational systems in real time.
Vikas Punna, CEO and founder of LakeFusion, said enterprises no longer face a pure data volume issue. According to Punna, the larger problem sits around data trust and consistency. He said LakeFusion was built specifically to operate inside Databricks so companies can unify and govern data without shifting it across disconnected infrastructure layers.
Silverton Partners led the round through its Austin-based investment platform, which manages more than $840 mn across seven funds.
Mike Dodd, general partner at Silverton Partners, said enterprise AI bottlenecks increasingly center on data fidelity rather than model capability.
According to Dodd, LakeFusion modernizes the traditional master data management category by transforming fragmented enterprise records into unified datasets directly where enterprise data already resides.
Carbide Ventures also joined the round. Pankaj Tibrewal, general partner at the Palo Alto-based venture firm, said many enterprises want to deploy AI systems but still operate with siloed data environments limiting model performance and operational visibility.
Master data management has existed for decades, though most legacy platforms gained a reputation for long deployments, expensive consulting cycles, and painful integrations.
LakeFusion wants to reposition the category around AI infrastructure rather than compliance-heavy back-office software.
The company already operates as an official Databricks ISV partner and appears on both Azure and AWS marketplaces.
Databricks-native infrastructure startups have gained attention as enterprises search for ways to keep AI workflows closer to existing cloud environments rather than stitching together separate governance and analytics systems afterward.
LakeFusion is betting enterprises prefer fixing the trust layer inside current infrastructure instead of rebuilding data architecture from scratch.
