Go from Data to Decisions, 5x Faster

Scribble Data empowers organizations to enrich their raw data and easily transform it to enable reliable and fast decision making for persistent business problems

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Feature Engineering

Quality and Trust

Consume & Collaborate

Driving decisions with Machine Learning at leading brands

Data-driven decision support for your business

A data to decision platform that helps you generate high fidelity insights to automate decision making.

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Faster Data to Decisions

Solve your persistent business decision-making problems instantly with advanced analytics powered by Machine Learning

Easier Data Consumption

Leverage customized data-driven workflows for easy consumption of data, and reduce your dependence on data science and machine learning engineering teams

Reliable Data, with all the Context

Rest easy and focus your energy on critical tasks, while Enrich does the heavy lifting to ensure availability of reliable and trustworthy data for decision-making

Operationalized Data in Weeks

Go from concept to operational data product in a few weeks, not months with Feature Engineering capabilities that can prepare high volume and high complexity data at scale

Solve High Impact
Decision-Making Problems
5x Faster!

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Customer Speak

"We found that the Scribble Data team understood our needs and pace as a fast-growing company, and were able to slot their Enrich feature store into our infra seamlessly. As a result, we were able to start ingesting data from our various sources, and delivering first cut models, with a lean team, much faster than we thought possible."

Peter Swaniker

CTO, The Room

"Working with large, disparate data sets that are captured at different points in time is never easy, especially when it’s for business-critical tasks. With Scribble Data, that’s no longer a challenge for us as it gives us access to reliable data with a high degree of auditability. It also significantly reduces our reliance on the central engineering team – so our bandwidth is freed to focus on solving business problems and use cases instead of infrastructural issues."

Vidyotham Reddi

Director, Growth Analytics
Mars, Incorporated

"Scribble Data’s engineering platform allowed us to translate our transaction overwatch ambitions into a self-improving data product. As a transaction-size-agnostic platform, our ability to foster financial inclusion rests directly on our ability to scale compliance outcomes. TIP pinpoints AML/CTF analysis., allowing us to spin up widgets for datasets, thereby making previously unsolvable problems solvable."

Akbar Hussain

Co-founder & General
Counsel, Terrapay

Powering the future of intelligent decisions

Welcome to the age of Sub-ML use cases

Let’s say you work at a modern data-driven company and you want to find a way to enhance one of your processes, like partner management. It makes sense considering you have limited resources to invest in partner development, but it ranks high on your growth goals for the year. The first step would be to […]

Hierarchical Features and their Importance in Feature Engineering

Feature engineering is both a central task in machine learning engineering and is also arguably the most complex task. Data scientists who build models that need to be deployed at large scales, across functional, technical, geographic, demographic and other categories have to reason about how they choose the features for the models. Despite the divergent […]

Growing Data Infrastructure Complexities

The world of data, and data infrastructure, has changed dramatically over the past decade. Traditional databases, which were designed to store information in a structured format, have evolved into massive warehouses of unstructured data that sit on multiple servers across different locations. Not too long ago, we were used to seeing monolithic systems dominated by […]

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