RetentionOS
Behavioral AI. 100% Local Processing.

Deliver what actually converts,
precisely when they're listening.

Beyond simple channel mapping. Our behavioral AI uncovers the exact copy variations and sent-times that drive individual purchase decisions.

Drop campaign CSV here

Supported format: Unified Campaign Activity Log (.csv)

XAI Recommendations

Every recommendation is backed by a clear explanation of engagement patterns.

Normalization

Automatically maps noisy channel and copy data into standard intelligence buckets.

Static Ready

100% client-side processing for instant results with no data leaving your device.

A/B testing is a lie.
Stop optimizing for the "Average User."

Traditional A/B testing forces a single "winner" onto 100% of your list, even though 40% of those users actually preferred the "loser." You're leaving revenue on the table by ignoring individual preference.

The Old Way: Majority Rule

  • ❌ Running "Winning" copy for everyone
  • ❌ Standardized 10AM send times for the whole list
  • ❌ Ignoring the nuance of copy-to-channel affinity

The New Way: Hyper-Personalization

  • ✅ Dynamic Copy: Offer vs. Content preference per user
  • ✅ Send-Time Optimization: Morning vs. Late Night affinity
  • ✅ Multivariate AI (Channel + Copy + Time)

The 1-on-1 Transformation Pipeline

  1. Upload your Campaign History. One unified CSV containing user activity, copy types, and conversion flags.
  2. Run Global Pattern Matching. Our engine correlates individual success across thousands of historical combinations.
  3. Deploy the Conversion Blueprint. Export the high-probability matrix to your ESP for a truly personalized campaign.

Frequently Asked Questions

"Does my data leave my computer?"

No. The entire Decision Intelligence Engine runs totally inside your browser. We couldn't access or see your customer purchase data even if we wanted to.

"What format should my CSV be?"

Just a standard export—it needs columns for user_id, channel, copy_type, sent_at, and clicked (boolean). You can download our sample CSV to see the exact structure required.

"How is the probability calculated?"

Our engine evaluates a weighted combination of channel affinity, copy engagement, and optimal timing based on empirical past user behavior to estimate future conversion probability.