Pepr AI provides an autonomous, cross‑channel quant‑marketing platform that uses AI agents to optimize paid media spend based on profit‑and‑loss drivers such as margin, inventory and customer lifetime value. The system integrates directly with mobile measurement partner data, runs real‑time A/B and geo tests, and continuously adjusts budgets, bids, pacing, audiences and creative within predefined guardrails, delivering a 10–15% ROI lift within two months for large consumer brands.
Funding
Funding not disclosed
Founders
Product
Problem
Consumer brands that allocate $5 M+ annually to paid media often rely on fragmented tools and proxy metrics, making it difficult to tie each advertising dollar directly to profit‑and‑loss outcomes. This leads to suboptimal budget allocation, delayed insight into incremental lift, and missed opportunities to improve return on ad spend.
Solution
Pepr AI offers an autonomous, cross‑channel quant‑marketing platform that aligns media decisions with core business metrics such as margin, inventory, promotions, and customer lifetime value. AI‑driven agents apply machine learning, causal inference, and risk‑aware optimization to continuously adjust budgets, bids, pacing, audiences, and creative rotation. The system integrates directly with mobile measurement partner (MMP) data and supports real‑time A/B and geo tests, delivering a 10–15 % lift in ROI within 60 days. Brands retain full control over creative and strategic guardrails while the platform provides daily incrementality reporting and evidence‑based autonomy that scales across major ad networks.
Target Audience
Primary customers are consumer brands and their marketing teams that spend $5 M or more annually on paid media and require P&L‑aligned, data‑driven optimization across multiple ad networks.
Features
- Business‑aware AI agents that optimize spend based on P&L drivers rather than proxy metrics
- Real‑time micro‑testing (A/B and geo) that measures lift across audiences, campaigns, and channels daily
- Risk‑aware optimization engine that balances performance gains with budget safety constraints
- Direct integration with MMP metrics for seamless data ingestion and attribution
- Autonomous high‑frequency adjustments of budgets, bids, pacing, audience targeting, and creative rotation within pre‑approved guardrails
- Continuous learning model that adapts to brand‑specific workflows, inventory changes, and competitive dynamics