String
Product strategy and model operations raised reported hit rate from 40% to 62% while scaling from zero to 5,000+ active traders.
A real-time Solana signal product translated trader behavior into ranked opportunities, improving reported hit rate from 40% to 62% while scaling to 5,000+ active traders and leaving an auditable eight-export model record.
Role
AI product strategistCo-founderModel operations
Outcome
A 0→5,000+ trader product with an auditable model-development archive and measurable improvement across its operating period.
The signal discipline
Better ranking beat more calls.
String worked backward from the routines of high-value traders to decide which symptoms in noisy on-chain activity deserved attention. Research became model features, opportunity ranking, and a fee model. As signal quality took priority over volume, reported hit rate rose from 40% to 62%, the platform reached 5,000+ active traders, and revenue reached roughly $250K. Eight normalized exports preserve calls, contracts, timestamps, model labels, and outcomes so precision can be assessed without one flattering metric hiding another.
At a glance
5,000+ active traders · 62% reported hit rate · ~$250K revenue
In the record
Model-version performance table · Maximum-X distribution + denominator · Call / leaderboard evidence ledger · Normalized eight-export dataset · Precision and call-volume analysis · Tail-exposure / maximum-X comparison
Preserve the development record
Eight exports were normalized into calls, contracts, model labels, timestamps, leaderboards, and outcome observations.
Compare like with like
Precision, call volume, average maximum-X, and tail exposure sit beside one another so no single metric can carry the story alone.
Keep the human in execution
The system narrowed and ranked opportunity; the trader remained the final actor rather than an invisible autonomous executor.