General Purpose Machine Learning API for Developers

No experience in data science required

 
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Create production-ready AI use cases in days 🚀

Wide-support for prediction, classification, anomaly detection, clustering, and other intelligent decision-making capabilities. 

Developer First

Developers are left behind in the AI revolution due to the complexity of data science. Same goes for product managers. We want to change that!

Fresh AI Approach

A general purpose AI technology alongside a simple data flow methodology simplifies AI  development. 

White-Box

Unlike black-box machine learning tools, thanks to our unique algorithmic approach decisions and results are fully explainable.  

 

API First

Our cloud REST API is available in the most popular programming languages covering in scope the full AI lifecycle of development, testing, and productization.

Runs Everywhere

Add AI capabilities to everywhere in your product/platform supporting public/private cloud applications & web services as well as mobile apps (Android, iOS).

Plug & Play Data

Native support for live and static data sources. Support for popular sources such as Facebook, Twitter, Salesforce, JSON, MySQL, MongoDB, Kafka, and others.

Frictionless AI Development

Our algorithm recognizes real-world phenomena as patterns inside data using a human-understandable language. Every AI task can be broken into a process of identifying patterns in the data. Our cloud API enables you to build complete AI and data pipelines and integrate the end result easily into your production code.

  • General Purpose AI
  • Expressive & Learning Modes
  • Explainable Decisions

Start with right data sources

Connect the relevant data sources using adapters. Your primary source should have records with natural order such as time series data.

Step #1

Define your task view point

Every AI task requires the relevant data to, whether in learning or decision making mode, and this is where the view comes into the picture.

Step #2

Create a Scanner

A scanner is where decision making and learning take place. You need to select the right scanner to start with fitting your AI task and set it's required parameters, and you're done. Our technology at the heart of the scanner works with explicit knowledge or automatically learned knowledge based on data from a learning view.

Step #3

Evaluate and Test

Testing and evaluation are quite straightforward. The scanner can set to run in a backtesting mode where you can instantly see its results and fine-tune it. The actual results, explanations for the results, and different performance measures allow you to pinpoint fast your learning workflow.

Step #4

Serve

Once you are happy with the results, insert the decision-making snippet into your production code, and you are done.

Step #6

API Examples

 

Trending Stocks

Predict when a stock is breaking out

Optimal Ski

Personalize ski vacations to users

Optimize Tweeting

Predict when is the best time to tweet

Protein Similarity

Classify proteins based on common behavior

Predict Customer Churn

Identify which customers are going to leave you

The Best NBA Player

Express your NBA expert knowledge

Tools

 

Traditional ML Vs. Braincast

Implementing AI using traditional machine learning (ML), including the latest offerings is slow, use-case focused and cumbersome, and requires in-depth knowledge in data science.

Traditional ML
Braincast
Effort
Data Integration
Data Engineer / Weeks
Developer / Day
Modeling
Data Scientist / Months
Developer / Days
Productization
Data Engineer + Developer / Weeks
Developer / Day
Maintenance
Data Engineer + Developer / Weeks
Developer / Days
AI
Human Knowledge Transfer
Indirect / Features & Labels
Direct / Non-trivial Patterns
Results Explainability
Limited / Black box
Transparent / Patterns
Task Complexity Level
Low / Static
High / Dynamic & Sequential
Support for Diverse Use Cases
Low
Max
Workflow
Modeling Knowledge
Knowledge from Training Data
Explicit Human Knowledge
Fine Tuning Decision Making
Tuning & Retraining
Explicit Modification of Reasoning
Evaluation & Testing
Slow & Batch
Live & Self Explanatory
 
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