AI retail ad analytics generated about $400,000 ARR in year one and helped unlock a Series A round.
- Year-one ARR
- ~$400k
- Funding
- Series A
- Pipeline
- 7-figure potential
What we shipped
A computer vision and predictive modeling platform that measures shopper engagement with in-store advertising and forecasts conversion impact of new designs.
Retail brands spend billions on in-store advertising such as shelf displays, posters, end caps, and promotional billboards, yet most still cannot measure how these visual placements influence purchasing.
Blueprint → AI Pilot → Production launch → Scale and operate.
We followed the Datablooz Delivery Model. See our process.
- Blueprint
Defined the merchandising decisions to support and the visual behavioral signals required to predict conversion.
- AI Pilot
Built computer vision models for shopper interaction analysis and attention modeling on real store video.
- Production launch
Shipped predictive conversion modeling and a simulation tool for testing shelf designs.
- Scale and operate
Extended to planogram compliance, customer flow, dynamic pricing, and digital twin merchandising.
Business, technical, and governance outcomes.
- Approximately $400k ARR in year one.
- Simulation testing of shelf designs pre-rollout.
- Series A funding unlocked by AI capabilities.
- Brand partners and investors engaged for growth.
- Python
- PyTorch
- OpenCV
- FastAPI
- PostgreSQL
- Kubernetes
Video analysis pipelines with privacy-aware behavioral aggregation and auditable predictions per design tested.
Working on something similar?
Schedule a call. We will tell you honestly whether AI is the right move.
Reference calls available under NDA after the second working session.