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AI engineering priorities for the Queen One platform. Three strategic directions for Maricor to evaluate and prioritize.
Building on the Tile Selector
This quarter, I completed the Tile Selector — an AI model that replaces random tile selection within dragontile flows with intelligent, per-user content matching. Rather than showing merchants' email recipients arbitrary content variations, the model learns which tiles resonate with which customer segments.
Below are three potential directions for Q2, each building on this foundation. They're designed as a menu for prioritization — we can pursue one deeply, two in parallel, or sequence all three across the quarter.
Three Strategic Directions
Each builds on the Tile Selector's intelligence in a different dimension of value.
Tile Gap Analysis & Auto-Generation
Use the Tile Selector's learning to identify where merchants' dragontile flows are underserving their customers. The model already knows which tile types perform well for which segments — we can invert that signal to reveal what's missing.
The first iteration surfaces a gap report: "Your Browse flow lacks product-education tiles for first-time visitors." The second iteration auto-generates tile suggestions to fill those gaps, so merchants only need to click "Approve" to close the loop.
Synthetic Data via Mirofish
As an upstart competing against Klaviyo, Queen One has significantly less historical data to train on. This gap compounds — less data means weaker models, which means slower merchant adoption, which means even less data.
We can break this cycle by using Mirofish (or a similar generalized synthetic data generator) to create high-quality synthetic training data precisely where we're weakest. Targeted synthetic augmentation for underrepresented verticals, customer segments, and interaction patterns could let our models punch above their weight class.
Offer & Discount Integration
Currently, the Tile Selector treats all tiles as if they differ only on content. In reality, tiles vary dramatically on economic proposition — a "10% off" tile versus a "new arrivals" tile aren't just different content; they're different incentive structures with different margin implications.
Incorporating offer mechanics (discount depth, free shipping, BOGO) into our models would let the AI balance engagement against merchant margin. This is a major blindspot today and a significant differentiator if we address it — Klaviyo treats all sends the same way.
Let's Pick Our Path
Each direction is scoped to be achievable within Q2. The question is which creates the most leverage for Queen One right now.