SABER — System for AI-Based Engagement & Recommendations

What SABER Is

SABER stands for System for AI-Based Engagement & Recommendations. It is a self-created, warehouse-native agentic decisioning engine built during my time at Hims & Hers Health—designed to automate lifecycle marketing experimentation and optimization with projected annualized revenue impact.

The Hims prototype proved the architecture with vendor building blocks. A later, independently evolved concept of the same capability lives at saber.danieljrobles.com.

How It Works

The preliminary build used Hightouch AI Decisioning and Google BigQuery ML infrastructure. SABER runs as a six-stage loop—Hightouch did not cover every stage end-to-end, but the prototype delivered enough to proceed.

1. Audience segmentation — define who enters the decisioning loop
2. Goal inference — infer the next-best outcome for each audience
3. AI-driven content assignment — select creative and offers via models
4. Multi-channel deployment — activate across lifecycle channels
5. Engagement feedback loops — capture response signals back into the warehouse
6. Continuous learning & optimization — retrain and retune from outcomes

Technical Stack

Deployed with Docker, GitHub Actions, and CircleCI. An internal UI/UX for operators was self-developed in Cursor so marketing and MarTech users could interact with decisioning workflows without living in raw vendor consoles.

Impact & Evolution

• Warehouse-native agentic decisioning for lifecycle marketing
• Projected significant annualized revenue impact via automated experimentation and optimization
• Vendor prototype (Hightouch + BigQuery ML) validated the six-stage architecture
• Operator-facing UI built for internal users to run and inspect decisioning
• Concept continued independently beyond Hims at saber.danieljrobles.com