Generate Inbound Leads for Your Home Service Business with AEO

Most HVAC, plumbing, and roofing businesses are stuck on a lead generation treadmill. The first stop is often lead aggregators like Angi or HomeAdvisor. You pay a high per-lead fee only to discover that same lead was sold to four of your competitors, triggering a price war before you even speak to the homeowner. The lead quality is poor, and you spend your margin just to win the job.

To escape the aggregators, many turn to Google Ads or a local SEO agency. With Google Ads, you might pay $100+ for a single click on a keyword like "emergency AC repair," and the leads stop the second you pause your budget. An SEO agency will charge $3,000 a month to write 4 generic blog posts like "5 Signs Your Water Heater is Failing." This content rarely ranks for valuable local terms and almost never converts into a phone call.

Consider an HVAC owner in Phoenix. They spend $6,000 a month on ads and an agency. They get clicks but half are outside their service area. The blog posts rank for nothing of value. They feel trapped because turning off the spend means the phone stops ringing. They are constantly feeding platforms they don't control, renting attention instead of building an asset. The structural issue is that all these channels are designed for a keyword-based search world that is rapidly being replaced. Blog posts are not structured for AI extraction, and ads are a temporary fix, not a long-term solution.

We built our own AEO engine because we saw this shift coming. The process begins by mapping the hundreds of specific, long-tail questions your potential customers ask. For a roofer, this isn't "roofing services"; it's "what is the cost to replace a 30-year architectural shingle roof on a 2,200 sq ft ranch house in Dallas?" We identify thousands of these granular questions for every service you offer in every neighborhood you cover.

Our technical pipeline uses the Claude API to generate structured, factual answers to these questions, which are stored in a Supabase PostgreSQL database. A Python script then assembles these components into machine-readable pages using FastAPI. Each page contains semantic HTML tables, detailed FAQPage JSON-LD schema, and a direct, citable answer snippet. This structured format is precisely what AI engines like Perplexity look for when they formulate answers and cite sources. The system publishes 75-200 of these pages per day.

For a home service client, the delivered system is your own automated, lead-generating asset. It lives in your own cloud account and continuously targets real customer questions in your service area. The leads that come from this engine are highly qualified. The prospect has already received a detailed answer to their specific problem, and your company was cited as the expert source. They call you pre-educated and ready to book a service, not just to price shop.