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Company Profile Founded in 2013, indico’s mission is to lead the next industrial revolution by helping people use powerful machine learning tools through an extensible and easy-to-use data augmentation platform. We’re lowering the barriers to the development, integration, and customization of machine learning algorithms by providing developer-friendly tools and APIs for building smarter software. We’re also dedicated to supporting our community with meaningful research and educational content. Today, machine learning requires a team of PhDs. indico dreams of a world where machine learning is so widespread, seamless, and human-friendly, it no longer seems like technology.

History

An indico Tidbit

Oct. 2013 Slater Victoroff and Alec Radford founded indico in an Olin College of Engineering dorm room

indico is passionate about machine learning to the core – so much so that it’s even reflected in our name.

Nov. 2013 indico successfully raises its first funding from Rough Draft Ventures June 2014 Diana Yuan and Madison May join indico as co-founders Aug. 2014 indico accepted into the TechStars Boston Fall 2014 class Nov. 2014 TechStars Demo Day: indico raises $3 million in seed round funding from General Catalyst, .406 Ventures, Boston Seed Fund, TwoSigma and angel investors Jan. 2015 indico graduates from TechStars and moves into its new home at 129 South Street in Boston

When Slater and Alec first started building this company, they brainstormed name after name before realizing that they really weren’t very good at coming up with names. The solution? Scrape several thousand top tech companies from Yahoo Finance, build a Markov model weighting letter transitions by market cap and then determine the most statistically successful name possible: indico.

Founder Bios Slater Victoroff Co-founder & CEO Slater loves explaining why machine learning is going to change the world. Ever since his first genetic algorithm figured out that 2+2=4, he’s been hooked. Having spent a previous life writing, with the occasional foray into theatre, he’s found his calling in spreading indico’s vision and getting everyone just as excited about what the future holds. When Slater’s not busy writing emails, he spends his time meditating, MMA fighting, and trying to help out on StackOverflow and Quora.

Alec Radford Co-founder & Head of Research Alec is addicted to exploring datasets and building practical products out of them, so his role as Head of Research at indico is pretty much the perfect fit. He spends a lot of his time exploring new areas of the field, leading machine learning workshops at conferences and generally teaching computers to do amazing things. When he can be dragged away from his machine learning projects, Alec loves scuba diving (particularly off the southern coast of Thailand), destroying the competition in Dota 2, and creating computer art.

Founder Bios Diana Yuan Co-founder & COO As our Chief of Operations, Diana runs a tight ship. She’s in charge of everything from telling us off when we’re not being good on Trello, to running leadership meetings, organizing events and generally making sure that things are operating smoothly across the company as we grow. Diana helped start indico while still studying at Babson College in Boston, MA. An influential leader, she had instrumental roles in building communities centered on entrepreneurship and community service. As President of E-Tower, Diana rebuilt the organization, one of the first and only live-in entrepreneurship communities in the world, while still fulfilling her responsibilities as President of Women Giving Back, a student organization celebrating equality and community service. Her personal motivations stem from her interests in human behavior, emotional intelligence and team dynamics. When she isn’t busy being a boss-lady, she loves free-styling new recipes, enjoying the outdoors, and traveling.

Madison May Co-founder & CTO Meet Madison, our Chief Technical Officer. He directs indico’s technological development, helps keep indico’s APIs running smoothly and coordinates with our marketing and user experience teams to translate user feedback into software infrastructure. Before joining indico, Madison taught computers English as a second language, and helped shift home energy systems into the digital age. He’s a firm believer in open source software, a contributor to a few popular open source projects (Python, Pylearn2, CommonRegex), and a strong advocate of user-oriented design. Besides being a machine learning enthusiast, this transplant from rural Virginia is an avid hiker and rock climber.

Our Tools & Services Pre-Trained Models Text Tags API: ⊲ Use Case: Create dynamic, customized filters for social media data to better understand your audience ⊲ Trained on a dataset of more than five million documents across over 100 categories Sentiment Analysis API: ⊲ Use Case: Improve consumer relationships by monitoring social media and detecting dissatisfaction or problems with your services ⊲ Achieves 90% accuracy on Stanford’s IMDB review corpus Language Detection API: ⊲ Use Case: Automatically suggest a user’s language preference based on a short passage of text. ⊲ Able to infer the correct language even if a word is misspelled, unknown to the model, or its accents have been stripped ⊲ Achieves 96% overall accuracy on 100 character sequences Facial Emotion Recognition API: ⊲ Use Case: Immediately understand how effective your banner ad is. ⊲ Recognizes six different emotions: happiness, sadness, surprise, neutrality, fear, and anger Political Analysis API: ⊲ Use Case: Quickly discover who your best political supporters are for improved campaigning

Our Tools & Services Model Refinement We can refine our pre-trained models to perform better on a business’ specific text and image problems with smaller amounts of data. This eliminates the nightmare of collecting and labeling thousands upon thousands of new data points for months – we can help businesses achieve the same results with just a few hundred examples in a fraction of the time.

Feature Extraction Image Features API: ⊲ Used as a preliminary preprocessing step for building other models with more applications, like categorizing images or recognizing objects ⊲ Transforms raw data inputs into a numerical representation that computers can understand ⊲ Can understand the physical features that are suggestive of a dog, like the structure of its face or the shape of its ears ⊲ Trained on a dataset of over one million images

Deployment Services GPU Parallel Processing: ⊲ Accelerate prediction and training by over 10x the norm ⊲ Currently, our Image Features API has GPU acceleration ⊲ Coming soon for our Sentiment Analysis, Political Analysis, and Facial Emotion Recognition APIs Batch Processing and Private Cloud Servers: ⊲ Special deployment infrastructure for businesses dealing with high volumes of data to handle projects of any size and complexity, with results delivered at nearly 25x the speed