MangoAI provides a reinforcement learning platform that automatically optimizes video ad bidding, audience targeting, and creative selection in real time. By treating each impression as an RL interaction, the system continuously learns policies that maximize metrics such as view‑through rate and return on ad spend, integrating directly with major DSPs for seamless campaign execution.
Funding
Funding not disclosed
Founders
Product
Problem
Advertisers struggle to maximize the impact of video ads due to static placement strategies and limited ability to adapt bids and creative parameters in real time, leading to suboptimal spend and lower conversion rates.
Solution
MangoAI develops reinforcement learning platforms that continuously experiment with and adjust video ad delivery parameters such as bidding price, audience targeting, and creative variants. By treating each ad impression as an interaction in an RL environment, the system learns policies that maximize predefined performance metrics like view-through rate or return on ad spend. The learned policies are deployed in real time within programmatic buying pipelines, automatically optimizing campaigns without manual intervention. Continuous feedback loops allow the models to adapt to changing user behavior and market conditions, improving overall advertising efficiency.
Target Audience
Primary customers are digital marketers and programmatic advertising teams at brands and agencies that run large-scale video ad campaigns.
Features
- RL-driven bid and budget allocation engine that updates policies per impression to maximize ROI
- Multi-armed bandit framework for real-time testing of creative variations and audience segments
- Integration hooks for major demand-side platforms (DSPs) enabling seamless deployment of learned policies
- Automated performance monitoring dashboard with metric-driven policy adjustments