8 buyers
$232 revenue
Compounder self-funding revenue agent
Same visitors in. The agent finds where revenue leaks, chooses the next page test, gets an independent model to judge the result, and shows how the next loop funds itself.
Agent run
Press Run the agent to watch the loop: diagnose the leak, choose the next test, approve the spend, and judge the result.
No extra traffic was bought. This sample case shows more value from the visitors already arriving.
8 buyers
$232 revenue
30 buyers
$870 revenue
$123 funds the next loop.
Net means $870 gross revenue minus the $25 test-mode spend approved through NemoClaw. The funnel numbers are a sample case. The Stripe proof is a real test-mode object, not a real customer charge.
How leads went up
The first page asked people to trust a broad promise. The agent's test chose one sharper page promise, one small action, and follow-up that matched the promise. In this sample case, visitor-to-lead moves from 8.2% to 15.8% and lead-to-buyer moves from 9.8% to 19.0%.
82 of 1,000 visitors became leads.
158 leads × 19.0% = 30 buyers. 30 buyers × $29 = $870 gross.
158 of 1,000 visitors became leads.
Why this stack
The loop needs memory. Hermes learns from experience, turns good moves into skills, and reuses them in the next test.
Basic math can spot a simple leak. Nemotron matters when the evidence gets messy: late buyers, emails, cohorts, and competing signals.
The agent should not have unlimited power. NemoClaw adds outside rules before actions: spend limits, test mode, data limits, approval gates.
Sales need receipts. Stripe records the money step, and test mode lets judges verify the payment path without moving real money.
What happened, step by step
The leak. 1,000 people visited, but only 82 left their info.
The fix. Hermes saved the lesson and chose one page test: a sharper promise, one call to action, and follow-up that matched the promise.
The safety check. Before the agent could spend, NemoClaw blocked the action and waited for human approval.
The learning. The agent saved the lesson: broad AI promises lost visitors. The next version should promise one clear outcome, a revenue-leak map, so more visitors know why to leave their email.
The proof. Stripe creates a real test-mode PaymentIntent a judge can verify in the dashboard, so the budget step is real, not drawn.
Learning ledger
Every run saves the observation, hypothesis, experiment, result, learning, and next action. That history becomes the growth memory.
Revenue-specific messaging performed better than a general AI promise, so the next test should keep the offer tied to one painful business outcome.
Judge test mode
Enter simple funnel numbers. Compounder finds the leak, chooses the next experiment, proves the test budget in Stripe test mode, and saves the learning.
Judge proof
The funnel is a sample case. This receipt proves the sponsor tools ran.
It reviewed the funnel evidence and decided which pattern to trust when buyers arrive later and follow-up changes the result.
This proves the payment connection works without charging anyone.
It turns what the last loop learned into one reusable next move: page promise plus follow-up test.
It gives the agent outside rules: what it can spend, send, access, or block before anything happens.
Compounder starts with the traffic already arriving.