About Bradley de Wet
Over a Decade in the Executor Seat
10+
Years building revenue engines
~$1M
Revenue saved from churn at Contactually
2x
Agency revenue as COO
Bradley de Wet, in his own words
I spent over a decade building revenue engines at high-growth startups, as the person doing the work. At Contactually I carried an inside sales quota and closed $318,000 in churn-adjusted ARR. I built the onboarding program that cut first-90-day churn in half, which saved about a million dollars in revenue over six quarters. I was the company's first recruiter and stood up the applicant tracking system. And I taught realtors, our customers, how to generate real business from their networks with CRM technology. At FiscalNote I was a client success manager with a portfolio that included Fortune 500 companies. I founded Tasting Club, a virtual tastings marketplace, and ran it for three years. Then I spent four and a half years as COO of iExcel, a digital marketing agency, where I doubled revenue, ran delivery, invoicing, hiring, and payroll, and executed marketing and sales operations for clients like Dapper Labs, Tock, and SalesIntel while I was there.
In January I left and gave myself one rule: build everything with AI, or do not build it at all. I was not setting out to start an AI automation company. I tested company ideas, field-operations systems for HVAC companies, a RevOps coaching business, and built every system AI-first. Teaching myself to build was not new: I built Tasting Club's product myself on Bubble, a no-code tool. What was new was how far AI took it. I built my marketing website from scratch with AI coding tools. I built a working diagnostic web app the same way, the one that runs my audits: it connects to more than twenty tools through their APIs and computes a maturity heat map from a client's actual stack. I wired my own operations to APIs and MCP servers for analytics, search, CRM, and publishing. Six months of doing nothing but building with AI later, an AI automation company stopped being an idea and became the obvious thing to build. That is how I became an AI guy: not by rebranding, by shipping. And because I have sat in the executor seat and rolled out new process to real teams, I know where adoption sticks and where it dies. So every automation I build ships with the adoption work on the other side.
Most AI automation fails for a boring reason: it is built on a broken operations foundation. The debt you could tolerate for years, dirty data, duct-tape process, fields nobody fills in, now decides whether AI works for you at all.
That is also the mechanism that decides who gets the gains. A company with a transformation budget pays somebody to fix the foundation and moves on. A company with forty people gets sold the tool and none of the repair. The tool does not work, and the conclusion everybody draws is that AI is not for businesses that size. The gap was never technical. It is who gets sold the repair.
We fix the foundation and build the automation on top of it, one named system at a time, at a published price, with your team owning it when the engagement ends.
The next three sections are Bradley’s account
Everything I build, I have run myself
That is the summary. Below is the long version of three roles, all at Contactually. Inside sales, customer onboarding, and a premium service I priced and sold myself. Every number in them is one I carried.
Inside Sales, Contactually
I doubled my conversion rate for some segments by questioning the demo that nobody questioned
2x
Conversion rate, some segments
$318K
Churn-adjusted ARR
~1.5
Avg calls to close
Contactually was a VC-backed CRM startup in DC. I was doing 30-minute screen-share demos all day, every day. The standard process was 3 to 4 calls spread across a 3-week free trial. Nobody had ever stopped to ask whether it actually needed to take that long.
So I started testing. I built demo accounts customized for each persona I was selling to. If I was talking to a real estate agent, the demo account looked like a real estate agent’s account, with email templates and workflows built around their specific pain points. Not a generic product tour. Their world, reflected back to them.
Then I added an offer at the end of every demo: sign up for an annual plan today and I will copy everything you just saw into your account by tomorrow morning. A lot of people said yes.
I doubled my conversion rate for some segments and closed $318,000 in churn-adjusted ARR. The sales cycle dropped from 3 to 4 calls down to about 1.5. I taught the technique to the rest of the team and the company shortened the trial period from 3 weeks to 2 weeks based on what we found.
Nobody at Contactually had ever timed the trial. Three weeks was in the playbook because three weeks had always been in the playbook, and testing it took the company down to two.
Customer Onboarding Manager, Contactually
I saved a million dollars in churned revenue by building what did not exist
~$1M
ARR saved from churn
50%
90-day churn reduction
+$720
LTV per customer
When I moved from sales to customer onboarding, I discovered that nobody had a structured process for what happened after a customer paid. There was no activation framework. No milestones. No data on who was actually using the product. Customers were signing up, getting confused, and canceling within 90 days, and no one had any visibility into why.
I started with brute force. I ran 2-call onboarding sessions with a subset of new customers. I tracked everything. The data showed the customers I was talking to were churning at a significantly lower rate than the ones I was not.
From that I built a hypothesis: there were 4 specific things a customer needed to accomplish to reach their first real moment of value. I called it the activation funnel. I oriented every onboarding call around getting people through those 4 gates, and the numbers confirmed it was working.
I hired and trained a team of 3. Tested outsourcing the calls to cut costs, then shut that down when I saw what robotic, checklist-following reps did to customer relationships. Pivoted to live webinars, then eventually automated webinars that ran as if they were live.
By the time the system was fully built, it had saved the company roughly $1 million in churned annual recurring revenue across 6 quarters. That result contributed directly to the company’s Series A valuation.
I ran the onboarding calls myself first, and the data from those calls is where the four gates came from. Only then did I hire the team of 3 to run them. Building it in that order is why it kept working once it was not me on the call.
Program Manager, Premium Services, Contactually
I built a premium service from scratch and priced it at 8 times the standard rate
$288K
ARR closed
40+
Customers on the premium plan
8x
Price premium
After proving I could fix sales processes and rebuild onboarding from the ground up, the company asked me to do something new: create a premium done-with-you service and take it to market.
Contactually’s enterprise customers were real estate brokerages who bought the software for their agents. The agents needed help actually using it. So I built a premium plan: weekly working calls with a success manager who would set up advanced automations in their account and show them how to run it.
I priced it at 8 times the standard subscription rate. Eight times.
I sold it through educational webinars where I would show the most advanced setups, things like automated open house follow-up sequences, and close with a simple offer: spend the next month figuring this out yourself, or let us do it with you.
I closed $288,000 in annual recurring revenue. The model worked.
The offer had a name and a number on it, and that is what made eight times the standard rate sellable. Every build Modern BizOps sells has its price printed next to it for the same reason.
Bradley, on the reason behind the company
AI is about to make a small number of companies enormously more productive, and most of them are already the biggest
The largest companies in every market have the budget for the transformation, the staff to run the pilot, and data that is already in decent shape. They are going to be fine.
The businesses that employ nearly half the private workforce in this country are getting pitched left, right and center on the promise of AI, and are buying services and implementation that often do not produce the value they were promised. That is not because the technology does not work at their size. It is because nobody will sell them the unglamorous part of fixing their underlying systems, and that unglamorous part is the biggest part of the job.
If this holds, the productivity gains from this decade land almost entirely on people who were already ahead. I do not think that is inevitable. I think it is a distribution problem, and distribution problems get solved when somebody decides to do the boring work at a price a small or medium size business can actually pay.
That is what Modern BizOps is built for.
The businesses I am talking about are not abstractions. They are the companies on the commercial strip outside your office. A staffing firm with forty people. A field services company running eleven trucks. A CPA firm that has done the same three things well for twenty years.
When one of them gets meaningfully cheaper to run, the money does not leave town. It turns into a raise, or a hire, or a price that holds while the competition raises theirs.
This is why the company is built the way it is. We publish prices, so you can decide without sitting through a sales process. We work on one named system at a time, so the bill is never a transformation. Your team owns what we build, and gets the training, so the capability stays in the building when we are no longer working together. We run audits that tell you what not to buy. And if there is nothing there worth building, you get your money back.
None of this is generosity in our minds. It is what the work has to look like if small and medium size businesses are ever going to capture any of the value from AI.
There is no client logo wall on this page
Modern BizOps is new. It has no case studies and no client results, and we are not going to borrow someone else’s, or dress up work our founder did under another company’s name as if it were ours. Everything above happened before Modern BizOps existed, in seats he held at other companies and in the one company he founded himself.
That is the whole reason the founding client program exists, and why it is priced the way it is. The first companies through the door are trading a shorter track record for terms nobody after them gets.
We are not for everyone. Here is who we are for.
If you are looking for a magic tool, or for AI to be the thing that finally makes a broken process work, this is not it. Automating a process nobody has defined just produces the wrong answer faster.
This works for founders and operators who will give the build a real system of record to sit on and one person on their side who owns it afterward. We do the building. Your team has to be willing to run it.
And we are not going to build something you stay dependent on us to maintain. Every system ships with a runbook and a handover to the person on your side who owns it. Every price we charge is published, so you can rule us out without booking a call.
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