Polis takes the analytical and logistical power of a presidential campaign and makes it scale for anyone.
Polis analyzes election data, demographics, and consumer behavior to automatically identify which voters are most likely to support you.
Dynamically generated walklists let volunteers take initiative. No more cutting turfs. No more manual data entry.
Polis learns from voters' responses, identifies trends, and suggests new strategies.
Polis automates research, logistics, and data collection so staff can spend less time behind spreadsheets and more time in front of voters.
Get automatic turnout and voting behavior forecasts within minutes of signing up.
Time saved managing lists is time better spent engaging voters.
Monitor canvassers' locations and progress in real-time.
Polis recommends new tactics to capitalize on observed trends.
We designed our mobile app to be intuitive for volunteers. No need for lengthy training sessions.
Connect Polis to your data on Nation Builder, NGPVAN, and other platforms.
Polis' targeting algorithms were designed by political consultants to emulate their own best practices. We ask candidates a few questions about themselves, then rank voters in their district according to support and turnout likelihood.
Combine voter data from many sources to prioritize which voters to contact.
Deploy canvassers to areas identified with high densities of desirable voters and let volunteers generate new walklists at their locations to canvass on their own schedules.
Canvassers engage voters with personalized scripts to discuss the issues most likely to persuade them.
Polis analyzes voter responses, updates campaign staff on progress, and fine-tunes targets to make the next round of canvassing even more accurate.
Campaigners, developers, scientists– we believe better politics begins with better campaigns.
Management consultant at Parthenon-EY and former political campaign manager, Columbia '14
Full-stack developer at CrowdComfort and former political speechwriter, Princeton '10
Analyst at Biogen, machine learning specialist, former quantitative linguistics research assistant, MIT '14
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