Enrich Intelligence Platform

Empower your data and analytics teams to use machine learning, minus complexities, to solve persistent business problems with ease and 5x faster time to value.

High-impact business decisions from reliable data, enriched with machine learning

Connect the dots between multiple data sources

Eliminate Infrastructure complexities and data silos

Build workflows to solve multiple analytical problems rapidly with Machine Learning

Enable better consumption of data to solve a wide variety of business use cases

Data is inherently messy.

Decision-Making for Persistent Problems with Data you can Trust

Data is pervasive. But without reliable data, your decision output ends up being inaccurate, unreliable, or worse - completely unusable. Scribble Data’s Enrich Intelligence Platform with its seamless workflows empowers your teams to not only trust but also transform, consume, and do much more with your data

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Highlights of the Enrich Intelligence Platform

How Enrich Intelligence
Platform Works

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Full-stack feature engineering

Enrich provides a full-stack feature engineering platform that enables you to go from concept to operational data product in a few weeks. It is ML without Ops. Solve multiple business use cases within an organization while optimizing for time to value despite resource constraints.

Flexible and extensible interface to scale faster

Enrich’s Python SDK allows users to develop, document, and test feature engineering modules, including transforms, pipelines, and scheduling; while also providing the flexibility to control their execution locally. A flexible and extensible interface also allows users to build reusable assets, embed domain knowledge, streamline processes, and roll out updates in a controlled and easy environment

Data discovery and consumption

Enrich provides hooks at each end to allow for easy cataloging of input data stores, and surfacing of features at any frequency through APIs. Downstream consumption of datasets and features is made easy, with flexibility to define data contracts and integration points.

Easily plug in your existing data infrastructure

From modern to postmodern or beyond, Enrich seamlessly integrates with any data stack. It connects to your data storage and processing infrastructure, and allows you to choose from a wide variety of options for decision-making or modeling.

Unify Your Organization with Data Driven Decision Making

Enrich helps you align your business goals with data understanding, empowering all your teams – internal and external, technical and non-technical. And the best part? Get up and running in as little as a few weeks!

Enrich for Business Users

  • Get ready-to-use, relevant, and reliable data that’s waiting to be analyzed
  • Easily transform analytical workflows from analysts into ML powered automated decisioning
  • Customize your data transformation efforts by bringing in intent and domain knowledge
  • Bring a high degree of transparency to your analytics efforts with auditability features
  • Easily consume your data and results using filters, search, and share with peers for structured collaboration
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Enrich for Developers

  • Quickly build applications that solve multiple analytical problems with Enrich’s low-code approach, minus the infrastructure complexities
  • Remove complexity from your datasets by easily eliminating duplicates, viewing a complete list of all datasets, and maintaining an ongoing index of datasets
  • Seamlessly transform raw data into enhanced feature engineered and reliable data for in-depth analysis
  • View and analyze errors right inside the platform to improve future products/workflows - 5x faster troubleshooting
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Resources to help you get started

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 […]

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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 […]

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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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