From data
to use case

Modular feature engineering

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Feature Apps Store

A one-stop shop

The Enrich feature store streamlines the handcrafting of production-grade features for each usecase. Data teams can collaborate on and reuse these features from a central feature marketplace. But Enrich can also bring the data to the doorstep of any user - with its extensible, modular design, Enrich allows for the building of lightweight data apps, that fit into users’ workflows, and give them access to the latest continuously computed features that they need. We call these Feature Apps - they include dashboards, intelligent reports, and search interfaces so that users can get to the latest transformed data when they need it.

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Build features.
Productionize your data.

Cut time-to-market.

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Your Features
Are Your IP

The Enrich feature store allows you to quickly turn your ideas about credit worthiness, or financial product affinity, or anything else into great features. Analysts and data science folks discover these and can reuse them across ML models or for deep analysis

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Trust
In Your Data

Setting data expectations, automatic data classification, trace how the data was transformed at every step, pipeline run checkpointing, reproducibility, generate out-of-the-box data processing records for GDPR compliance

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Efficiency

Automatic data classification, faster debugging, internal marketplace of features for reusability, just-in-place video integration for commentary capture alongside every dataset

Why Customers love Scribble

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Akbar Hussain

Co-founder & General Counsel 

A Fin tech Payment Gateway Customer

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"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.”

Deploy in minutes

Terrapay has been taking rapid strides in expanding their fintech business, both across geographies as well as product offerings. They work with partners like Western Union and Transferwise among several others, and needed a feature engineering platform that would transform their raw data to power usecases like their cutting edge anti-money laundering approach and their partner benchmarking, among others.

Latest from Scribble

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Welcome to the
age of Sub-ML use cases 

Learn more about the Sub-ML approach to solve business use cases which allows you to show quicker results as you adjust for deficiencies in real time, as opposed to an ML approach which poses multiple constraints for you to build a production grade model.

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Hierarchical Features and their Importance in Feature Engineering

Feature engineering is a central task but also the most complex task in ML. Learn how hierarchical features can add value to the data science lifecycle, and how feature hierarchies can improve MLOps productivity.

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Mid-Scale Production
Feature Engineering

Watch this Virtual fireside chat between Dr. Venkata Pingali, Demetrios Brinkmann, and the MLOps community about mid-scale production feature engineering and how we built the architecture for Enrich, our feature engineering platform.

 
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