machine learning Startups

DataFox is an AI-powered prospecting platform that started at $49/month but now charges customers $10,000-$200,000 annually by targeting enterprise buyers with annual contracts. The co-founders, led by Bastiaan Janmaat (ex-Goldman Sachs), raised $9M and grew through programmatic SEO pages covering 2 million businesses combined with manual data labeling to train their machine learning algorithms. The company serves major customers including Twilio, Box, and Salesforce.

APSEN is an AI-powered SaaS platform that automates expense audits and back office finance functions for mid-to-large enterprises (500+ employees). Founded in 2013 by Anant Kail and co-founder Kunal Verma, the company achieved nearly $1M in ARR by 2015 with 22 enterprise customers at $50K average contract value, having raised $3M in venture capital. Their enterprise-focused cold sales approach yielded an $18K CAC with zero churn and strong product stickiness.

Ripple Match is a SaaS platform that automates college recruiting by connecting employers with qualified candidates regardless of school, founded in 2015 and hitting $125k MRR across 75 paying customers. Starting from $4,500 MRR 18 months prior, the company scaled through annual subscription contracts (averaging $25-30k) with a machine learning matching model that achieves a 60% first-round interview rate and 1-in-28 hire rate. With $3.7M raised and zero churn on their current product, they're focused on scaling interview connections and expanding their candidate base through campus ambassadors.

SiteWit is a self-serve marketing platform that automates Google AdWords and Google Shopping campaigns for small businesses, built by co-founders Ricardo Lasa and Don over three years starting in 2010 and launched in 2013. The company grew from $150k MRR in August 2017 to $300k MRR through partnerships with major website builders like Wix and Weebly, serving over 10,000 paying customers with an average spend of $30-60/month net. With 20 employees in Tampa and $7M raised, they're closing a $5M Series B round at a $36M valuation, achieving 100% YoY growth with 3% monthly churn among paying customers.

Daisy Intelligence, founded in 2003 by Gary Sarenvarada, uses machine learning and reinforcement learning to help retail and other enterprise clients make smarter operational decisions around pricing, promotion, and inventory. The company has 17 enterprise customers paying an average of $20,000/month and has achieved a $4 million ARR with 100% year-over-year growth and 110% net revenue retention. Gary is raising $10 million at a $40 million pre-money valuation to scale across new markets and industries.

Skyhive is a machine learning-powered competency discovery tool founded by Sean Hinton that helps enterprises understand workforce capabilities, identify skill gaps, and create upskilling pathways. Launched in April 2017, the company scaled from zero to 17 enterprise customers generating ~$85k/month in revenue through inbound leads and the Singularity University network. The company raised $1.5M CAD in seed funding and expects to reach profitability in Q3 2019.

Harvester is a product management platform that centralizes user feedback, feature prioritization, and roadmap communication in one place. Founded by Valentin Huang and a team of three, the company launched in early 2018 with $15k in grants and love money and has grown to 100+ beta users in France. They're measuring stickiness through daily usage metrics and plan to launch pricing in summer, targeting starting plans at €100 for small teams.

Segment helps mid-market banks (under $100B in AUM) understand and categorize transaction data using machine learning and AI-powered rules engines. Founded by Rob Heiser, the company has grown from 12 customers in October 2018 to over 100 banks serving 10+ million customers combined, charging 6-20 cents per customer per month. The company raised $30 million, grew approximately 100% YoY in 2019, and projected 120-200% growth in 2020 despite COVID-19 disruptions.

Ansaro was a HR-focused SaaS that aimed to use AI and data science to improve hiring and interviewing processes. Despite raising $3M and growing to 6 team members, they failed to achieve product-market fit after 2 years and multiple pivots, earning only $100K total revenue against $70K monthly expenses before shutting down.