Scribble Data, an ML feature engineering startup has raised $2.2 million in seed funding led by Blume Ventures. The round also saw participation from Log X Ventures and Sprout Venture Partners, in addition to participation from Vivek N. Gour (former CFO, Genpact) and Ganesh Rao (Partner, Trilegal).
This seed round will be used to build out Scribble’s product roadmap, and to build a strong presence for Scribble Data in the North American market as they grow their customer base. The product roadmap includes a low code consumption interface for teams to access and use features produced on the platform, as well as additional apps that bring data teams closer to specific solutions like anti-money laundering, benchmarking, personalization, and recommendations, among others, the company said in a statement.
“With this fundraise, we will be doubling down on hiring the right talent to help deepen the Enrich feature store, as also to ensure world class product support for our customers,” said Indrayudh Ghoshal, co-founder, Scribble Data. He further added, “The problems of data lacking consistency, context, and credibility are fairly common. We’ve seen great organic growth over the last year, and this fundraise will help us address the growing interest in our platform.”
Scribble Data is an MLOps product company. Their modular feature store, Enrich, comprises a number of pre-built feature engineering apps to help data teams cut time-to-market for each data science use case including unified metrics, customer behavioral modeling, and recommendations.
Taimur Rashid, Chief Business Development Officer for Redis said, “Collaborating with Scribble Data’s Engineering team, we successfully deployed Redis’ high-performance entity matching solution for a joint customer where Enrich handled integration with multiple data sources, data prep pipelining and orchestration, as well as batch and streaming feature engineering. Using Redis’ in-memory vector similarity lookup, Enrich achieved a ~30x speedup on entity match generation. This opens more ways for developers and organizations to use Redis’s capabilities for machine learning use cases, so we’re looking forward to working closely with Scribble Data to address customers’ machine learning workloads.”
Enrich streamlines the data prep process with versioned pipelines, delivering continuously updated data through intuitive interfaces and surfacing context around datasets via extensive metadata and lineage tracking. With offices in Toronto and Bangalore, Scribble Data has clients across 4 continents.
Anirvan Chowdhury, VP, Blume Investment team said “With more organizations effectively becoming data companies, there is a proliferation of high quality, compliant feature sets for ML and Sub-ML use cases in an organization. And those feature sets will need to be managed, re-used and served in the most effective manner into ML models or other Sub-ML use cases.”
He further added, “We’re excited to back Scribble Data, whose novel approach to hard problems in deep tech will serve a wide variety of use cases for global markets. We particularly liked Scribble Data’s modularized Feature Store approach and an App Store with the Enrich Feature Store as a backbone to solve for end to end use cases. The market is in its early stages, but we believe it will expand significantly with applications being built on top of the Feature Store and are super excited to be a part of this journey.”