8:42 AM - Inbox Triage
I open the Compliance Radar dashboard in a new tab. I do this every morning now, same as checking my email. The habit has already formed in the six weeks since I acquired this engine.
Seven new signups overnight. That's $1,400 in new ARR sitting in the pending activation queue. Three of them are already live because they auto-provisioned. Four need a manual onboarding call or at least a walk-through email. I add them to a Slack thread I maintain called #pending-activations. My outbound AI agent will draft personalized onboarding sequences for each one later today, but for now I just log the source: two from LinkedIn, one from a content piece about FDA guidance, four from a legal ops community Slack I joined last week.
Today's revenue is $2,840. That's total MRR added this month so far. It's not big, but it's real.
I open my Gmail and scan for the overnight alerts. Slack has pinged me twice with flagged regulatory changes from the system. One is a DOJ directive on telehealth privacy standards. One is a state-level policy shift in California around data retention for legal firms. Both are probably legitimate flags. Both probably matter to customers. I open a second browser tab.
Stripe dashboard. I'm checking the monthly recurring revenue chart and looking for churn. I see one cancellation hit last night: Carol Reyes at Reyes Family Practice. She was on month three, paying $200 a month. I remember her now. She went through the onboarding, set up the integrations, then probably realized she needed a different kind of product or decided the price wasn't in her budget. I make a mental note to send her a closing survey. Not to win her back. Just to know what didn't work.
Churn rate this month is still near zero. That's the volatility I'm watching for.
10:15 AM - A Flagged Conflict
The system has prepared draft regulatory summaries for both overnight flags. I read through them in the admin dashboard. The DOJ directive one is clean: clear summary, relevant audience flagged, three action items listed for customer review. My AI agent nailed it.
The California one has an edge case flagged in yellow. The policy affects legal firms and in-house counsel at certain corporations. But the scope definition is ambiguous. My agent has drafted two possible interpretations and flagged it for manual review.
This is the work. Not watching the AI do everything. Making judgment calls on the fuzzy stuff.
I open the actual PDF of the guidance document. It takes me twelve minutes to read and think through the details. The ambiguity is real. I land on this: we flag it for both audiences, but we add a note that legal firms should review the scope with their state bar, because the definition might exclude certain practice types. I go back into the admin dashboard and edit the summary. I add a single sentence to the legal ops section of the flag. I mark it as reviewed and published.
Both flags are now live and pushing to the 340 customers who've subscribed to the relevant alerts. That's what the numbers tell me: I have about 340 customers total right now across all alert categories. I know this because I look at the dashboard every morning.
An email notification comes in from one of yesterday's customers: Marcus Chen at Chen & Associates. He's writing to say that the FDA guidance alert we sent three hours before the mainstream legal tech press covered it caught a compliance gap in his contracts. He'd already scheduled a meeting with his team to address it. He's thanking me by name.
I take two minutes to write back. I thank him. I ask what alert categories he's most interested in going forward. I plant a seed about his team potentially using the product themselves, or his partners. I don't sell. I just listen. I send it off.
12:30 PM - Lunch and the Pipeline Check
I step away for lunch. I make coffee and a sandwich and sit on the back porch for fifteen minutes. The numbers in my head: 340 customers, $68,000 in ARR, $1,400 in new ARR yesterday, one churn in the last week. The baseline feels stable.
When I come back, I open our CRM spreadsheet. I keep it simple because this is still a one-person operation. It's a Google Sheet with columns for prospect name, company, title, deal size, stage, and last touch. I have 48 prospects in various stages of conversation. Eleven of them are in the demo scheduled stage. Four are in proposal sent. One is in about to close.
That one is Sarah Thompson. She's a Chief Compliance Officer at a mid-market staffing firm. She took a demo three weeks ago, asked good questions, and I've sent her three follow-ups since. Today I see that the deal is marked proposal sent, awaiting feedback. It's been five days since I sent the proposal. At $300 a month, this is a good deal to close.
I open Gmail drafts and look for what I sent her. The proposal was solid. I offered a thirty-day trial and a custom onboarding call. I did not offer a discount. I decide to send her a soft follow-up. Not pushy. Just checking in. I write three sentences: I wanted to see if you had any questions about the proposal. I'm happy to walk through the implementation timeline or talk through how your team would use the alerts. Let me know what works for your schedule.
I send it at 12:58 PM.
2:08 PM - The Billing Question
A customer named David Winters emails in. He's been with us for four weeks. He's complaining that he was charged twice for his first month, and he wants a refund for one charge.
This is the kind of thing the AI can't handle without my input. It needs judgment and context. I open my Stripe dashboard and search his email. I can see his subscription history. He subscribed on April 8. There are two charges: one on April 8 and one on May 8. That's the monthly billing working correctly. But he might have been double-charged during the signup flow.
I review the actual transaction notes. It looks like there was a payment processing retry on April 8 that succeeded after the initial charge. So yes, he was charged twice on the same day.
I go back to Stripe and issue a manual refund for $200. It takes ninety seconds. Then I write David an email. I explain what happened, confirm the refund, apologize for the confusion, and let him know we've fixed the billing on his account so it won't happen again. I also manually refund him through the dashboard so he'll see it reflected tomorrow.
It's manual work, but it's necessary. The kind of thing that builds trust.
I also make a note in Slack that we should audit the payment flow for duplicate charges. I don't fix it myself, but I flag it for the developer I've contracted to handle bugs.
4:30 PM - Pipeline and Close Work
Sarah Thompson hasn't replied to my lunch follow-up. It's been three and a half hours. That's fine. I check my pipeline spreadsheet again. The eleven demo-scheduled deals have an average deal size of $250 a month. If half of them close, that's $1,375 in new ARR. If all of them close, it's $2,750. That's the variance I'm managing.
I spend thirty minutes on outbound. I pull the list of prospects who took demos but haven't engaged in two weeks or more. There are six. I open Gmail and draft a personal note to each one. Not a template. Something specific to each person based on the notes I took during their demo. To one, I reference a specific challenge they mentioned about FDA guidance. To another, I mention that I think their team might benefit from the state-level alert feature they asked about.
I don't send them all at once. I space them out, three now, three tomorrow. The goal is to look like a person, not a mail merge.
6:15 PM - Wrap
I close the Stripe dashboard and pull up the Compliance Radar admin dashboard one more time. I check the health metrics: 340 customers, 98.1 percent uptime, 12 new flags processed and published today, 2 manual reviews completed.
The day is honest. I did not sit back and watch an AI run everything. I reviewed and edited a regulatory summary. I fixed a billing error with my hands and Stripe. I wrote four customer emails. I sent two follow-ups to prospects. I triaged seven new signups.
The AI amplified my work. The agent drafted the onboarding sequences I'll review tomorrow. The system flagged the edge case I needed to resolve. The alerts were packaged and delivered without me touching them. But the decisions, the judgment calls, the relationship work. That was me.
I feel good about the growth. Seven signups in one night feels like validation. Carol Reyes' churn feels like feedback I need to understand. Marcus Chen's thank-you note feels like proof that the product actually works.
It's 6:22 PM. I close the laptop and head to dinner.