Industries NewsProfessional NetworkingProfessional ServicesSocial MediaHeadquarters Regions San Francisco Bay Area, West Coast, Western US Founded Date Jan 1, 2017 Founders Michael Zammuto Operating Status Active Last Funding Type Series A Also Known As Completed
Company Type For Profit
Contact Email support@completed.com
Completed's mission is to create the most comprehensive profile of an individual on the internet. A Completed Profile will show the full person including constructive feedback that identifies their strengths and weaknesses, helping people improve themselves while at the same time opening the door to opportunities for other people to engage
with them in a positive manner.
With the advent of search engines and social media, there is now more than ever an overwhelming abundance of information on the internet about people.
Companies, when doing their due diligence, have to spend countless hours researching people to find out information about them. What if there was a website where people went to first where companies could find all of a person's social media, bio, feedback, and more? What if that site empowered the individual to showcase their talents to the world, increasing their odds of landing a new career or getting picked for a special event.
Completed.com is on a mission to create the most comprehensive source of information about an individual on the internet. We seek fair and unbiased reviews of people, while at the same time giving the individual the ability to take a high-level of control over their brand on the internet.
Completed.com's founder and CEO Michael Zammuto and team includes experts in startups, online review sites and reputation, internet publishers and B2B SaaS companies. Zammuto says the Completed.com is to make society a true meritocracy where you can rate anyone in business, where top performers get the recognition they deserve and where all professionals can get trusted, constructive and verified feedback that helps professional development.
Completed.com's platform leverages big data with descriptive and predictive analytics and then using machine learning to analyze, score and implement user generated reviews and data service sources to provide a better quality, more reliable technology-driven approach to human-driven reviewsystems.