8:42 AM - Inbox triage
I open the dashboard at 8:42 and the first thing I see is a Slack alert from 4 AM. Two new signups overnight. Both from Shopify stores, both in the apparel space. $198 combined MRR if they stay. Second month of actually running this, and the motions are starting to feel less like luck.
I click into the CRM first. Seventeen qualified leads in pipeline, three of them demoed last week and waiting on follow-ups. I've got a reminder to send a check-in to Marcus Lee at Ascend Activewear by today. The AI drafted an email last night after I fed it the context from our last call. I pull up the draft in Gmail.
The email is solid. Conversational, references his specific problem - losing margin because he's reactive on pricing during sales instead of proactive. The AI nailed the tone. I make one small edit, change "our platform" to "the system," hit send. It's 8:47 now.
I glance at the Stripe dashboard in a new tab. MRR is running at about $8,200. Revenue today is $0 yet, which is typical for a Tuesday morning. Week to date is $640. I'm tracking toward about $37k for May if the close rate holds. Still quiet compared to the $96k Year 1 target I'm supposed to hit, but the demos are converting at about 40 percent now, which is better than the 25 percent I was seeing in month one.
10:15 AM - A flagged conflict
The AI system flagged something at 9:50. I see the Slack notification. One of our customers, Carol Reyes at Reyes Family Practice, had her subscription canceled by our payment processor this morning. No fraud flag, no obvious issue. Just a failed payment on her card. She's been a customer for 31 days, still in the onboarding phase, not even using the system live yet.
I open her customer profile. Notes show she demoed with me five weeks ago, closed at $99 a month, got her Shopify store connected, and was supposed to launch pricing rules yesterday. The failed payment was her second billing cycle. If I don't move, we're down 80 bucks in MRR and a customer who never really got the value they paid for.
I send her a Slack message first since I have her workspace connected. No response yet - it's before 10 AM on a Tuesday, she's probably working. I draft a manual email explaining what happened, that we caught it, and offering to walk her through the first launch today so she can see the tool actually working for her store. This is the work the automation doesn't do. I add her to my calendar at 2 PM. Sometimes the save is just showing up.
11:30 AM - Review and approve
Three more AI-drafted outreach emails hit my inbox. These are cold reaches into the Facebook groups where e-commerce store owners hang out. The AI has been trained on our best conversations and is reaching out to prospects we've identified as good fits - stores between 10 and 100k monthly revenue, selling inventory-heavy products.
First email to Janice Kim at Blueprint Home Goods is sharp. It leads with a specific problem about margin erosion from competitive pricing pressure, then offers a demo. I approve it. Second one to David Okonkwo at Okonkwo Fabrics feels slightly too formal, takes too long to get to the ask. I rewrite it, tighten it, send the revised version out. Third one is good, approve and send.
This is the pattern of my mornings. I'm not writing from scratch. I'm editing, approving, redirecting. The AI is doing the volume, and I'm doing the judgment calls. This is the only way I'm getting to 20 demos a month without hiring a full-time SDR at this stage.
I check the calendar. Four demos scheduled for next week. Two are first-time prospects, two are follow-ups with people who demoed last month and are still in consideration.
12:30 PM - Lunch and the metrics check
I grab lunch and pull up the analytics dashboard while I eat. I've built a simple one in the product itself that shows me today's performance at a glance. New users, activations, revenue, churn. The two signups from overnight are in the "connected" state, meaning they've successfully linked their Shopify stores to us. Neither has run a pricing rule yet, so they're still in the critical first-48-hours window where most new customers either click around and get hooked, or open the browser tab once and forget about it.
I make a mental note to check on them again tomorrow. For today, I've scheduled automated onboarding emails to hit their inboxes in two hours. The AI generates those too, but I reviewed the templates last week and they're solid. Personal, not pushy.
Week to date: $640 in new MRR. That's on track for $2,600 in new sales for the month if we hold the pace. At $99 per month, that's roughly 26 new customers for May. I'm currently at 18 total active customers, so we'd be doubling the base by month end. That's aggressive but not impossible given the pipeline.
The hard number staring at me though is churn. I lost one customer last week, and now Carol's payment failed. Small base means volatility. Lose two more and May starts looking like a wash.
2:08 PM - Customer escalation
Carol got back to me. She said the payment method issue is real - her card got deactivated last week and she forgot to update it. But here's the actual question she asked: "Before I pay the fee again, can you tell me if this actually works for my store? My margins are so thin that if this doesn't increase sales or cut my discount rate, I need to save the hundred dollars."
This is the conversation no AI should have alone.
I hop on a 20-minute Slack call with her at 2:30. She's running a practice with four locations, sells skincare products online, and is getting crushed by big-box retailers in her category who can price lower. What she doesn't realize is that Pricing Intelligence doesn't just cut her discounts - it shows her where she can raise prices without losing volume, based on what her own customer data says. She's been measuring herself against the wrong benchmark.
By 2:55, she understands the play. We schedule a 30-minute screen share for Thursday morning where I'll walk her through loading her first month of inventory data and running a pricing rule against a single product category. She'll actually see the numbers. She asks me to update her payment method so we don't lose the subscription. I walk her through it in Stripe. We're back to active.
That call probably saved 80 dollars in MRR and planted a customer who might actually succeed.
4:30 PM - A bug I need to fix
I'm reviewing the Linear board - my support and product tracking tool - and there's a ticket flagged high from a prospect who demoed last Thursday. His Shopify store has 45,000 SKUs. When he tried to load his inventory file into the system, the upload timed out. Linear shows me the note: "Takes over 300 seconds, likely memory issue in the background job."
This is a real blocker. If he can't get his data in, he can't demo the actual tool. I'm not going to build something fancy to fix this at 4:30 on a Tuesday, but I can do the fast version. I check the upload code, find the bottleneck - the job is loading the entire CSV into memory before parsing it. I swap it for streaming parsing, rebuild, and deploy to staging. It's a 15-minute fix and a 30-second test confirms it works.
I message him in Linear: "Got the fix in. Can you try uploading again?" He comes back in eight minutes. Upload completes in 52 seconds now. He says "that's the move, let's demo properly Thursday." That's another qualified lead who nearly dropped out. This part of running the business doesn't scale automatically. You have to stay in the system.
6:15 PM - Wrap
I close the laptop at 6:18. The day was solid. Two signups that need to be converted. One customer saved from churn. One bug that could have killed a deal, killed instead. One demo locked for next Thursday.
What worked: staying responsive to problems instead of letting them sit. Sending Carol a message instead of just canceling the sub. Fixing the upload bug myself instead of assigning it to a backlog. The AI drafted emails cut my time in half, but I still had to edit and send. Nothing runs fully on autopilot yet.
What was hard: watching the revenue numbers and knowing I need to double the customer base to hit the Year 1 target. Realizing that most of the work is still on me. The AI amplifies what I do, but it doesn't replace the demos, the customer calls, the judgment calls about which prospects are actually worth reaching out to.
Two months in, the system is working. It doesn't feel like it's running itself. It feels like I'm running something worth running.