Ashley Ingram.

Small home-service businesses — HVAC shops, plumbers, electricians running a few trucks — lose real money every day to one thing they can't fix: they miss calls. The owner is under a house or on a roof, the phone rings, no one answers, and the caller dials the next company. For a trade where a single job is worth thousands, that unanswered ring is direct, daily lost revenue.

The usual "fixes" don't land. Hiring a receptionist costs $3,000–$4,000 a month plus training and turnover; the $49 AI-receptionist apps ask a non-technical owner to configure software they don't trust and won't maintain. Both leave the bleed open.

Jessica is the answer I built: an AI voice employee that catches those calls and turns them into booked jobs — sold on a pay-only-when-she-books-a-job model, so the owner takes zero risk to say yes. The interesting part isn't the tech list; it's the design decisions that remove the exact reason a buyer would walk at each stage.

Jessica isn't one thing — she evolves across the whole relationship. Same voice, three jobs:

Everything runs on one engine — the same tools handle two situations: a sales demo (someone signs up) and a live missed call (a real customer). Here's how the software connects, left to right.

Same engine, two triggers — nothing is duplicated. Think of Make.com as the wiring that connects everything, and Airtable as Jessica's memory.

So I built the demo around a reverse-demo approach — letting the prospect experience the product before hearing a word of sales. An owner submits their name, business, and website; within 60 seconds Jessica calls them and answers as their own receptionist, greeting them by their real business name. She fields the call as if the owner were a customer, books a realistic appointment using genuine knowledge of their business, and only then reveals she's AI.

Letting a buyer experience a product before selling to them isn't a new idea — the hard part, and the part I'm proud of, was engineering one that actually works: personalized to their business, in under a minute, fully automated. By the time the reveal lands, "I don't believe it works" is already dead — they just watched it work on themselves. The sale moves from "convince me" to "how do I get this."

Each of these removes a specific reason a buyer would walk — and each is a judgment call, not a wiring task.

The demo is just the front door. Once an owner signs on, Jessica is designed to run as their around-the-clock voice employee. This is the roadmap I architected — some pieces live, some in development:

Painters lose 30+ minutes writing every estimate by hand, and slow, inconsistent quotes lose jobs to whoever replies first. Pro Quote turns a spoken job note into a professional, branded, editable quote in minutes — the pro records a quick voice message (or fills a simple form) and a polished quote lands in an organized folder, ready to send.

Sifting job boards for the right roles is slow, repetitive, and easy to do badly. This is a Python agent I wrote that continuously scans job feeds, filters by role and location, removes duplicates, then uses an LLM to score each role against a candidate profile — logging only strong matches to an Airtable pipeline with apply-status tracking. It shows I can architect and ship a working agent in real code, and that I think about cost and reliability while doing it.

Designed as the next build (not yet shipped): for every strong match, automatically tailor the résumé to beat the job's ATS keyword filters and draft a custom cover letter — turning the agent from a job finder into an end-to-end apply assistant.