Manual quoting was costing one HVAC company hours every day, and deals were slipping through the cracks because follow-ups came too late. We rebuilt their entire proposal process using a custom Laravel application with an AI drafting layer, and the results were hard to argue with. Here’s exactly what we built, how it works, and what changed for their business.
The problem: a quote machine that ran on elbow grease
Our composite client, let’s call them Piedmont Comfort Systems, a mid-sized HVAC and mechanical contractor serving the Charlotte metro, was doing solid revenue. But their sales process had a serious drag on it.
Every time a new commercial prospect came in, a project manager would spend 45 minutes to an hour pulling job details from an email or phone call, cross-referencing equipment pricing in a spreadsheet, writing a proposal in Word, exporting it to PDF, and emailing it manually. They were sending 15 to 20 quotes a week.
Do the math: that’s up to 20 hours a week of skilled labor spent on formatting documents.
Worse, quotes were going out 2 to 3 days after the initial inquiry. By then, a competitor who moved faster had often already won the job. Their close rate on new commercial leads sat around 22%.
They came to systemsevendesigns with one request: make this faster without hiring another person.
What we built: a Laravel app with an AI proposal layer
We built a custom web application in Laravel, a PHP framework well-suited for business logic-heavy tools like this. No off-the-shelf software, no duct-taped Zapier chains. A purpose-built internal tool that fits how their team actually works.
Here’s how the workflow runs now.
When a prospect calls or emails, the project manager opens a structured intake form inside the app. They fill in job type (new installation, retrofit, maintenance contract), square footage, system type, site complexity, any special requirements, and contact details. It takes about 5 minutes.
Once the form is submitted, the app sends the structured data to an AI layer built on OpenAI’s API. We wrote a carefully tuned system prompt that gives the model context about the company’s services, tone of voice, standard terms, and pricing logic. Within seconds, the AI drafts a complete proposal: executive summary, scope of work, line-item pricing pulled from a connected pricing database, timeline, and a call to action.
The project manager gets a notification, opens the draft, reviews it, makes any edits, and hits send directly from the app, which fires a branded PDF to the client. Total time from form submission to sent proposal: under 15 minutes on a complicated job, under 8 on a standard one.
If the prospect hasn’t responded in 48 hours, the app triggers an automatic follow-up email. No one has to remember to chase it.
The technical specifics (without the jargon)
The Laravel backend handles authentication, form logic, and database operations. Proposals and client records are stored in a MySQL database. The AI call is made server-side using a queued job so the interface stays responsive even if the API takes a moment. The pricing database is a simple admin panel the team manages themselves. They can update equipment costs without touching code.
We also built a basic dashboard showing open quotes, sent proposals, follow-up status, and won/lost outcomes. Nothing fancy. Just visibility they never had before.
The whole build took eight weeks from discovery to deployment.
What changed: the numbers
Three months after launch, here’s what Piedmont Comfort Systems reported:
Quote turnaround dropped from 2 to 3 days to same-day in 90% of cases. Time spent per proposal dropped by roughly 75%, from about 55 minutes to under 15. Close rate on new commercial leads rose from 22% to 31%, a 41% relative improvement. And in the first full quarter post-launch, zero quotes went without a follow-up. Before, an estimated 20 to 30% never got one at all.
The project manager who used to spend Monday mornings buried in Word documents now uses that time on site visits and client calls.
What this means for your business
You don’t have to be an HVAC company for this to apply. If you run a service business, whether landscaping, construction, IT services, consulting, or home renovation, and your quoting process involves someone manually assembling documents from scattered information, you have the same problem.
The AI isn’t replacing your expertise. It’s taking the formatting, the boilerplate, and the follow-up scheduling off your plate so your team can focus on work that actually requires human judgment.
A tool like this isn’t a six-figure enterprise software project. For a business doing 15 or more quotes a week, the return typically becomes obvious within the first quarter.
If your sales process has a bottleneck that looks anything like what we described above, systemsevendesigns builds exactly this kind of custom tooling for businesses across the Charlotte region. Start with a conversation. We’ll tell you honestly whether a custom build makes sense or whether a simpler solution fits better.