Solo-built and shipped Flowmation — a production AI automation system for real-estate lead-to-cash. From problem to pipeline to a data-grounded optimization program: designed, engineered, and run by one person.
Agents lose deals to whoever replies first. A lead that waits hours is usually gone — but an agent can't leave a showing to answer every inquiry. Flowmation closes that gap: it responds to every inbound lead in under a minute, follows up until they answer, and runs the whole funnel — sourcing, engagement, and measurement — without manual work.
Everything. Problem discovery and positioning, the system architecture, the code, the outreach copy, the experiment design, and the analysis. I defined what success looked like, built the thing that pursued it, and instrumented it so the results could be read honestly.
A structured experimentation program — hypothesis-driven, statistically framed (sample sizes, base rates, expected outcomes), one variable at a time — so the funnel improves on evidence rather than instinct.
Each of these is a working component I designed and shipped — not a mockup. Together they form the pipeline above.
An inbound lead triggers a personalized reply that's written and delivered in under 60 seconds — webhook → Claude API → Gmail. Built and verified end to end, so no inquiry ever sits cold.
A public, self-serve demo where anyone can submit a lead inquiry and get a personalized, agent-voice AI response in their inbox in under 60 seconds — webhook → Claude API → Gmail, with bot protection and abuse guards so it's safe to expose publicly. The product proving itself, live, before a single sales conversation.
Routes leads to isolated variants by cohort while holding the message body as a controlled constant — so a subject-line test measures the subject line and nothing else. Designed for clean causal reads.
A documented, repeatable method that sources targeted leads and validates 100% of them for deliverability before a single send — with query variations rated by yield so the process compounds.
Time-triggered follow-ups that keep going until a lead replies — then stop the instant they do. Ships with a kill switch, failure logging, and an audit trail that reconciles every send. Fails loud, not silent.
One controlled content pipeline across web and email — generation, brand consistency, and versioned publishing — so what ships is on-brand and on-message everywhere, every time.
Pulled the LLM out of the send path when a fixed template served the experiment better and cheaper. Knowing when not to use the model is as much the job as knowing when to.
The moment a prospect books, an automated, personalized confirmation fires — closing the gap between a booked call and one that actually happens. Built after a real no-show exposed the gap, it turns a leaky handoff into a reliable one.
The habits that show up in everything above — and the reason the work holds up when you look closely.
Every call framed with the math — sample sizes, base rates, expected outcomes. Forecasts before results, so we optimize on evidence, not vibes.
Clean experiments only. When a subject line is under test, the body is a controlled constant — so a result actually means something and can be trusted.
Nothing ships on trust. Live test-sends, config checked against source, audit trails reconciled. "It should work" isn't the same as "I watched it work."
Kill switches, pristine rollback baselines, failure logging, syntax-checked deploys. Systems designed so the expensive mistakes are hard to make.
I'd like to help you ship it — and measure whether it worked.