Inbox and follow-up
Important messages wait, and follow-up keeps pulling someone back into the inbox.
AI-first from the inside out
Return your focus
to growth.
Clone Your Genius captures the standards behind recurring approvals, reviews, and corrections, then installs them as AI-first operating patterns.
Built by Gar Lee, who has led support, onboarding, customer success, fulfillment, and automation systems across Talkable, Lottery.com, GrowFlow, and Daily.ai.
THE OLD OPERATING MODEL
More tools. Same loop.
Every week brings a new model and another must-try tool. So someone tests it, writes prompts, and compares outputs.
Then the same approvals, reviews, handoffs, and exceptions still come back to the same people.
OUTCOME BEFORE SYSTEM.
Start with one stuck path.
We pick one repeated path, capture what good looks like, and build the checks it needs to move without constant rescue.
01
Find where focus is trapped
Identify the recurring loop stealing owner or senior-operator attention from growth or higher-value work.
02
Capture the operating standard
Map the rules, examples, context, risk flags, approval points, and escalation paths that make the work good.
03
Install the AI-first flow
Build the system that prepares, routes, checks, escalates, and improves the work so senior attention returns to growth and higher-value work.
USE CASES
Where attention gets trapped.
Recurring drag usually shows up in context, routing, standards, approvals, exceptions, and follow-through.
Client onboarding
Every new client creates the same chasing, checking, routing, and handoff drag.
Approvals
The same decisions keep returning to the owner or operator before anything can advance.
Support and success
The standard stays trapped in Slack, memory, old tickets, and repeated replies.
Quality checks
Senior attention gets pulled into rework because the standard is not captured clearly enough to follow.
Exceptions
Edge cases keep landing on the same senior people because escalation paths are not systemized.
Built by someone who's done the work.
Gar Lee has built and led the operating functions where growth promises get kept or broken: onboarding, support, customer success, fulfillment, customer experience, and automation.
At GrowFlow, Gar built Support and Customer Success while ARR grew from $1M to $13M before a $60M exit. At Daily.ai, he built onboarding, success, support, and fulfillment operations while the company scaled to $4M ARR. At Lottery.com, he led customer experience in a regulated environment where speed and compliance both mattered.
FIT CHECK
Fix the work that keeps coming back.
Good fit if
- Recurring work keeps pulling the owner, founder, or senior operator away from growth or higher-value work.
- SOPs exist, but execution still depends on memory and review.
- Growth or higher-value work is constrained by approvals, exceptions, follow-up, or quality checks.
- You want AI installed inside live work, not tested on the side.
Poor fit if
- You want a prompt pack or generic automation agency.
- You are not ready to expose how the work currently gets done.
- You want AI to run without inspection, review, or standards.
- The repeat work is not painful, frequent, or valuable enough to systemize.
FAQ
Questions and answers.
How is Clone Your Genius different from an AI consultant?
Clone Your Genius helps businesses become AI-first from the inside out.
Gar brings 20 years of experience building systems, processes, automation, fulfillment, customer success, and support operations. Clone Your Genius uses that experience to redesign what gets captured, routed, automated, reviewed, and improved.
The result is an operating model built for AI from the foundation up, not a few workflows bolted onto the old way of working.
Will this replace my team?
No. The win is freeing senior people for the work that grows the company. They spend less attention on repetitive loops and more attention on higher-impact work.
My work is too custom. Will this work?
Custom work still has repeated patterns: context, standards, routing, approvals, exceptions, and follow-through. We start where that pattern creates the most drag, then capture how the loop should run.
We already wrote SOPs. Why would this be different?
SOPs document steps for a person to follow. We capture how the work gets done and build the agent that runs it.
What do I inspect before I trust this?
The Growth Review, the operating-model map, the install plan, and the measurement plan. The method is visible before you commit.