HOW DID WE GET HERE?

OUR FOUNDER'S STORY

From academia to business

Brad was doing his PhD at MIT in machine learning and AI when he started TrueMotion, now the market leader in the US telematics auto insurance space.

Starting state: lots of data to label, no viable solution on the market

We were in an 18-month RFP process to win a contract with Progressive in which 11 competitors were participating. To win we had to label a ton of noisy, complex, and poorly structured data sensitive to errors. We tried to use existing labeling solutions and libraries, but they didn’t work.

We looked at labeling companies on the market at the time and found none were able to help:

- The interfaces were static and too complex
- AI automation didn’t work
- Training the crowd took a lot of effort
- Performance between different people was highly variable and we needed to track it ourselves
- Could only use one type of crowd
- Errors were hard to catch and fix
- No trainable parameters to improve the system

Ending state: we built an in-house labeling solution

We developed our in-house labeling solution adjusted to our needs, use cases, and edge cases. What we realized after building this is that there were a lot of other data science teams that had the same needs, and that there was a strong market need for our solution.

What we built provided a great feature set:

- Train the crowd with less effort
- Automatically qualify new crowd on the task
- Distribute tasks to best performing crowd and measure individual performance over time
- Improve crowd performance by building expertise on a subset of tasks that can be used in multiple use cases
- Automate much of the labeling process

Super.AI technology offering

Super.AI provides the same technology that we built and used to deploy machine learning algorithms to the below insurance companies at TrueMotion (many customers not public yet).

Built BY RESEARCHERS from

Team

Meet the rest of our team

We experienced AI in different ways in our careers, we came together to allow companies outside of the Google's of the world to use AI.

Lars Wulfken

VP Product
Vice President Product Management at super.AI. Former Head of Product Management for Commerce Platform and Principal Product Manager for Amazon Web Services (AWS) in Berlin. Prior driving the development of a company-wide, globally deployed Platform as a Service (PaaS) in the cloud technologies organisation at Adobe.

Enrique Garcia

Principal Engineer
Enrique is the Principal Engineer at super.AI. Prior to this, he was an ML Research Engineer at SAP, where he contributed to projects related to Transfer Learning, Meta Learning, Source Code Analysis and Healthcare. Enrique was also a Research Associate at MIT.

Eddie Vaisman

Head of Customer Success and Sales
Eddie is also a CoFounder and Technical Advisor of Stat Zero, which helps enables governments to build startup ecosystems. At TrueMotion, a Harvard innovation lab startup, Eddie developed signal processing and machine learning algorithms to make drivers safer as a data scientist and first hire. He then switched to product and created a product line that detected car crashes which led to winning three $1M contracts with top 10 insurers within 24 months.

Adriana Puchianu

Marketing

Lisa Torlina

ML Researcher

Purnawirman

ML Engineer

Herry Sutanto

Engineer

Michael Lee

Product Manager

Investors

Backed by amazing investors

We are fortunate to have an exceptional set of investors backing us. Chances are you use the products they help build.

Toby Coppel

Co-Founder & Partner
Mosaic Ventures

Julie Sandler

Managing Director
Pioneer Square Labs

Grant Ries

CEO
Liveramp

old & new

A new programming paradigm for AI

Making AI available to everyone

50 years ago

Only the smartest people in the world could use computers, which were expensive and could only be programmed in binary. Over time people built abstractions: assembly, compiled languages, interpreted languages, GUI. Now almost anyone can use a computer. This changed the world.

Today

Only the smartest and most well funded companies can effectively use AI. Just like 50 years ago, when computers only understood binary, today's AI only understands labeled data. At super.AI, we built abstractions on top of labeled data: AI assembly language, AI Compiler and data programming.

Vision

But we aren't even close to being done. We made big steps with the AI Compiler and data programming, but our mission is to ultimately make AI available for everyone.

Gui
AI for everyone
Api
Marketplace
Compiled language
Data Program
Assembly language
Labeling primitives
Machine language
Labeled data
Old way
New Way
Old
Way
NEW
way
Gui
AI for everyone
Api
Marketplace
Compiled language
Data program
Assembly language
Labeling primitives
Machine language
Labeled data

Old way

New way

Label data randomly
Pray it works
Label more data if doesn’t look good
failure rate
90%
Enterprise AI projects
Quality
Speed
Satisfaction
Flexibility
Decompose problem into reusable program
The AI compiler automatically:
– Finds and labels only the most valuable data
– Routes only to optimal labelers
– Improves over time
– Update labels incrementally
– Finds and labels only the most valuable data
failure rate
90%
Enterprise AI projects
Efficiency
100%
Fastest way to train AI

Old way

Label data randomly
Pray it works
Label more data if doesn’t look good
Quality
SPeed
SATISFACTION
FLEXIBILITY
failure rate
90%
Enterprise AI projects

New Way

Decompose problem into reusable program
The AI compiler automatically
Finds and labels only the most valuable data
Routes only to optimal labelers
Improves over time
Update labels incrementally
Quality
SPeed
SATISFACTION
FLEXIBILITY
efficiency
100%
Fastest way to train AI