The impact of AI in the enterprise has been very limited to date, with only a small percentage of businesses able to generate real ROI from their efforts. Despite the tremendous opportunities, users continue to struggle with enterprise AI applications for a variety of reasons:
- Skill requirements – Few organizations have the expertise on staff to build and/or deploy a solution themselves. These skills are increasingly difficult to find outside the organization as well.
- Lack of good training data – Unless you are Facebook, Google or Amazon, putting AI into production for unstructured data has proven to be difficult because of the massive amounts of data required.
- Data format – Over 80% of the data in most enterprise is in the form of unstructured content. This data has tremendous value, but few AI solutions are well designed for it.
- Finding the right use case – Most companies still lack a good framework for identifying the right use case candidates for applying AI to unstructured content.
- Disconnect between Data Scientists and Line of Business – Even at companies that have made investments in a Data Science team, there is still a significant gap between their efforts and the needs of the business. Too often, Subject Matter Experts aren’t part of the problem definition and approach, so Data Science efforts go to waste.
Indico is helping users overcome these challenges by making the application of AI more practical and efficient in the enterprise.
Since our founding in 2014, we’ve been on the forefront of innovation in Enterprise AI with a deep learning technique known as Transfer Learning, an approach perfectly suited to the challenges of unstructured content. We’ve focused our efforts on lowering the barriers to entry for AI and accelerating enterprise success by requiring less data, time, expertise, and investment to get real business results. Indico’s approach eliminates much of the trial and error typically required, reducing wasted effort and speeding up time to value. We’re fundamentally changing the risk/reward ratio for businesses and equipping them with the full power of AI.
Today, thousands of users have deployed our solution in a variety of use cases to help automate tedious back-office tasks, improve the efficiency of labor-intensive document-based workflows, and extract valuable insights from unstructured content.
Tom Wilde, CEO
Tom brings 25 years of experience in solving the complex problems of digital content to the role of CEO of indico. Prior to joining indico, Tom was the Chief Product Officer at Cxense (see-sense), a leading Data Management provider and founder of Ramp, an enterprise video content management company. Tom also held senior roles at Fast Search, Miva Systems, and Lycos. He has extensive experience with company building and venture-backed startups and holds an MBA in Entrepreneurial Management from Wharton.
Slater Victoroff, Founder, CTO
Slater has been building AI, machine learning and deep learning solutions for the enterprise for the better part of the past decade having worked with everyone from the federal government to two-person startups to the Fortune 100. Slater has educated hundreds of business users in the successful implementation of deep learning through a simple framework that helps executives rapidly accelerate the adoption of the technology in their businesses. Slater attended Olin College of Engineering before pursuing Indico full-time after its acceptance into the Techstars Boston Program.
Scott Money, VP of Engineering
Scott is a software engineering leader with 22 years of experience in the development of digital products and SaaS platforms for some of the world’s most popular brands. At Indico, he leads the development and expansion of the company’s machine learning solutions for unstructured content. Prior to Indico, Scott was VP Product, Video at Cxense (see-sense), a leading Data Management provider, and VP Software Engineering for Ramp, an enterprise video content management company. He has also served in senior software development roles at ITA Software and Lycos.
Diana Yuan, VP of Talent and Operations, CoFounder
Diana keeps Indico running on time and on budget. She oversees all operational aspects of the business including HR & recruiting, procurement & budget management, communications, and facilities & infrastructure. She helped found Indico out of Babson College where she studied technology, design and entrepreneurship and served as a mentor in the Foundations of Management and Entrepreneurship (FME) program.
Madison May, ML Architect, CoFounder
Madison is the Machine Learning Architect at Indico where he has played a key role in developing the company's enterprise AI platform for unstructured content. Prior to joining indico in its early days, Madison designed and built an NLP system at Fetchnotes and was an active open source contributor to projects like Python3, Pylearn2, and Theano.
406 Ventures is an early-stage venture capital firm that invests in innovative information technology and services companies founded by the finest entrepreneurs. The firm’s veteran team of industry entrepreneurs, operators and investors apply real world experience, deep industry knowledge and networks, and strong company-building skills to create value for entrepreneurs and investing partners. 406 typically are the lead, first institutional investor in early-stage and de novo investments in market-changing companies and have been early investors in machine learning and data science. Sample portfolio companies include Bedrock Data, Business Intelligence Advisors, Reltio, Revmetrix, and Simon Data.
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Indico Data Solutions, INC.
186 South Street Ste 400
Boston, MA 02111