We’re two operators who build AI that does real work inside real businesses: the reporting, the busywork, the systems that don’t talk to each other. Put AI to work on what slows you down.
A few examples of what we’ve built inside operating businesses. Your problem doesn’t have to look exactly like these.
A wellness brand started every morning copying figures out of six tabs and still couldn’t tell which number was right.
A nightly pipeline pulls store, ads, refunds, and bookings into one warehouse and publishes a single scorecard by 7am.
16+ scattered reports became one screen, fed by 12+ live sources.
The scorecard shows sales, ad spend, refunds netted out, and consults booked. Nobody assembles anything; it is simply there when the team arrives. Each source is refreshed overnight and the report shows which ones loaded, so a stale feed is visible instead of silently repeating yesterday’s number.
A B2B retailer’s support team answered the same thirty compatibility questions all day, while buyers who couldn’t wait simply left.
An AI agent in the storefront answers from the live product catalog, asks the questions a good salesperson would, and hands off to a person when it should.
Answers come from live product data, and the team reviews what it handled and what it got wrong.
The agent reads the same catalog the store sells from, so compatibility answers reflect current products rather than a script. When a question needs judgment, volume pricing, or a human relationship, it says so and hands the conversation to the team.
A brand that sells and rents a device had three versions of every month: the store said one thing, the ops system another, the books a third.
A monthly tie-out checks every charge, refund, return, and rental buyout across all three, and a daily sync now fails loudly instead of quietly.
In two closes it caught 3 dropped sales, $545 of refunds booked as revenue, and a feed that had been dead for five weeks.
The store, the operations system, and the bookkeeper’s books never quite agreed, and each close was a week of “why is there an extra $600?” The tie-out now says exactly where and why they differ. It also produces a payout-by-product report the accountant had been told the platform couldn’t provide.
You work directly with Chris and Rick, from figuring out the problem to getting the solution running.
Chris spent 14+ years running e-commerce as a VP and P&L owner before building systems full time. He builds what you see on this page: reporting pipelines, AI agents, internal tools, and automations. For clients, he scopes the fix, builds it, tests it against real data, and keeps it monitored after launch.
→ LinkedInRick spent 20 years as an e-commerce CFO, owning the close, forecasting, and the reporting a leadership team runs on. For clients, he makes sure a new system produces numbers you can act on, keeps the scope tied to a business outcome, and brings the operating discipline that keeps growth from outrunning the business.
→ LinkedInEach of these runs inside a real company today. Open one to see more.
A scheduled scraper watches thousands of competitor prices and saves a screenshot for every one it reports.
An industrial distributor priced thousands of SKUs against competitors who changed prices silently. Checking by hand never happened.
A scheduled scraper that walks competitor sites on its own. Validation checks reject a page that doesn’t parse as expected, so a broken page is flagged instead of reported as a price, and a screenshot is saved as evidence for every price that is reported. The pricing team opens a dashboard, not forty tabs.
Every reported price carries its own evidence capture.
One CRM holding 10,000+ orders, with the ship queue the warehouse actually runs from.
Orders lived in one system, fulfillment in another, and the team lived in spreadsheets between them. Nothing agreed with anything.
One CRM holding 10,000+ unified orders, with a ship queue the warehouse runs from, address editing that syncs back to the carrier, and label printing built in. The spreadsheets were retired.
10,000+ orders unified. The whole team runs on one live screen.
The month-end close lands as an interactive dashboard instead of a long email.
Every month the financials went out as a long, text-heavy email. Hours to write, skimmed in seconds, and no way to ask “why is that up?” without starting a reply-all thread.
The close now produces an interactive dashboard instead of a wall of text: revenue, margin, cash, and spend against plan, with the trend behind every number one tap away. It assembles itself from the same figures as before; the finance lead reviews it and hits send. It is a single self-contained file that travels inside the company’s own email and access controls, with no new service touching the books.
One of us wrote that email for years. Now the month lands as a dashboard, and the questions arrive pre-answered.
AI writes a one-page brief before every consult from the survey, the booking, and the order history.
A brand runs video consults all day, and every call started cold. Practitioners had two minutes to skim raw survey answers across three generations of intake forms, often in three different places. Whether the person ever bought was anyone’s guess.
One record per prospect: store survey, booking slot, order history, and any earlier call transcript. Before each consult, AI turns that into a one-page brief covering what hurts, what they’ve tried, and what to ask. After the call the transcript files itself, and the outcome (bought, rented, returned) updates from the order system with no one typing it.
598 surveys from three forms, one record each. Every consult opens with a brief already written.
An embedded editor lets the team change 3,000+ products without a developer ticket.
Every catalog change, whether a spec, a filter, or a description across 3,000+ products, was a developer ticket with a two-week fuse.
An embedded editor inside their own store admin: search, stack filters, bulk-edit, apply. The team ships its own catalog changes now, and the dev queue lost its longest line.
3,000+ products, edited in-house, without a ticket.
Email, calendar, reminders, and research in one assistant you can call or message, in daily use since February 2026.
A CEO’s day was scattered across an inbox, a calendar, a reminders app, and a browser, with nothing that knew all four and could act on them from the car or a meeting.
Smarty: one assistant reachable by voice, text, or browser that reads the inbox, drafts replies and reschedules, and sends only after reading them back. It runs a morning briefing, a 4pm open-items list, and reminders on the local clock wherever he travels, and it can be re-tuned or switched off by talking to it. Every draft is confirmed before it is sent, enforced in the code, not left to the model.
Confirm-before-send, tuned by talking, hourly self-checks, and it runs on hardware the client owns.
An eight-module console wired to Amazon’s own APIs lets one in-house operator run the account.
A brand paid an agency to manage Amazon and still couldn’t answer basic questions about its own account, listings, or ad spend.
An eight-module operating console covering sales, ads, listings, inventory, and reclassification, wired to Amazon’s own APIs. One in-house operator now runs the whole account, and the agency was retired.
8 modules, one operator, one less agency invoice.
Every package is month to month, with hours reserved for your work each month.
For one priority at a time: one system built, tested, and kept running.
Discuss your prioritiesFor several active priorities, or one larger implementation you want finished sooner.
Discuss your prioritiesFor a program of work across the business, with both of us involved most weeks.
Discuss your prioritiesAdditional hours are $175 per hour, when available.
You get experienced operators part time, without a full-time hire. You pay for the specific work you need instead of a salary and a hiring cycle. We do this for AI systems, operations, and finance.
Repetitive work that should run on its own, numbers you can’t fully trust, and systems that don’t talk to each other. The examples above are the shape of it: reporting, customer-facing AI, reconciliation, order systems, and internal tools your team can run.
Yes. Helping your team use the solution is part of every build, and we design tools so your people can operate them without a developer on call.
It depends on what’s broken. Some of the systems on this page were useful within days; others took months of iteration. We’ll tell you which yours looks like, along with the proposed scope, before you commit to anything.
The monthly fee covers our team’s time. Most of what we build runs on tools you already pay for or on services that cost little or nothing. If a solution would need substantial AI usage or a third-party subscription, we agree on it with you in advance.
Engagements are month to month, and what we built stays with you: the code, the accounts, and the documentation. Some work is a limited project with a clear finish, and the engagement ends when it is done. Systems you rely on every day usually need ongoing maintenance and support, and we can keep that running at whatever level fits.
Tell us about one process you’d like to improve, or just that you want to talk about what AI could do for your business. You don’t need a specific problem in mind. We’ll help you figure out whether we’re a fit.
Or email us directly at
[email protected]
