Systems we've designed and built.
Diagnosis, decision, and delivery aren't just described elsewhere on this site: they're demonstrated here. Each of these started as a specific operational problem, was mapped and designed before anything was built, and was verified against real data once it was.
Turning every inbound inquiry into a consistent, documented decision
The problem: Every inbound lead is a judgment call: how serious, how good a fit, how urgent. Handled manually, that judgment depends entirely on someone being available to review it, and it varies by whoever happens to be doing the reviewing.
What was built: A system that reviews every submission the moment it arrives, applies the same explicit scoring criteria every time, and writes a structured, auditable decision straight into the CRM, with no submission waiting on availability and no lead judged by a different standard than the one before it.
Verified: Tested against real submitted data, with every decision cross-checked against a full audit log, so the standard being applied is consistent and reviewable, not a black box.
Built with Tally, Make, Anthropic Claude, HubSpot, and Google Sheets.
Closing the gap between “signed” and “properly onboarded”
The problem: Once a prospect agrees to become a client, four separate things have to happen correctly: the CRM updated, the team notified, a kickoff task created, a welcome that matches what was actually purchased. Handled manually, all four depend on one person remembering to do them, in order, with no visibility into what's been missed until a client asks why no one's followed up.
What was built: A single handoff sequence that runs automatically the moment onboarding is complete, performing all four actions correctly and routing to one of two different welcome experiences depending on what was actually purchased, with each step designed to fail safely on its own rather than stalling the whole sequence.
Verified: Tested end-to-end across both engagement types, confirmed directly against CRM records, task associations, and the messages actually delivered.
Built with Tally, Make, HubSpot, and Zoho Mail.
Making three judgment calls correctly before a meeting gets booked
The problem: A booking form only solves half the problem. Someone still has to judge whether a lead is ready to talk, check whether the requested time is genuinely free, and route the outcome correctly: three separate judgment calls usually made by hand, where any delay costs momentum and any missed conflict costs a double-booked hour.
What was built: A system that checks real calendar availability before confirming anything, and hands off to a human at exactly the point a human judgment call is actually needed, rather than guessing.
Result: “The lead isn't lost or double-booked. It's routed to a human at exactly the point where human judgment is actually needed.”
Built with Tally, Make, and HubSpot.
Following up with warm-but-not-ready leads, without anyone having to remember to
The problem: Not every lead is ready the moment they submit a form. Left unattended, those leads either get chased inconsistently by whoever remembers, or forgotten, and a CRM full of stale “maybe later” contacts makes it harder to see who's actually still warm.
What was built: A scheduled follow-up cadence that uses the lead's own CRM status as the control for what happens next, and retires the lead cleanly if nothing changes after a set window. The first design used a different trigger method that didn't behave as intended in testing; it was rebuilt around a schedule instead, which is the version actually running: a real pivot, not a claim that everything worked on the first attempt.
Result: “The CRM status acts as the control state for the nurture sequence.”
Built with Make and HubSpot.
An automated answer that only speaks when it actually knows
The problem: Customer questions repeat constantly, but every one still needs a human to read and reply, even the simple, predictable ones. The obvious fix, a chatbot, creates a worse problem: a system that sounds confident even when it has no real basis for the answer.
What was built: A decision layer that answers only when a real, approved source supports the answer, and routes everything else to a human with full context preserved, deliberately built to admit uncertainty rather than bluff.
Result: “An automation that's honest about what it doesn't know is more trustworthy than one that always sounds confident.”
Built with Make, Anthropic Claude, and HubSpot.
See how this would apply to your business
Every system above started with a diagnosis, not a tool. That's the same starting point for any engagement.
Start the Diagnosis