Lease Solar Energy
Contract
A lead-generation site for a South Florida residential solar lease provider, serving homeowners across Miami-Dade, Broward and Palm Beach. The offer is a $0-down lease where the provider owns and maintains the system, so the site has to qualify homeowners and route them into the client's CRM without a salesperson in the loop.
The problem
Solar is a category where the buyer arrives suspicious and under-informed. They have heard a pitch before, they cannot easily tell a lease from a purchase from a PACE assessment, and the honest answer to most of their questions is conditional. A brochure site converts none of them. It also has to compete on search terms that are almost entirely question-shaped — what a lease is, whether panels survive hurricanes, whether a particular roof qualifies — which is exactly the territory answer engines and generative results now occupy.
Approach
- 01
Built the eligibility check as the primary action, so the visitor's first step answers their own question rather than surrendering a phone number.
- 02
Wired qualified submissions into the client's CRM over an API, so the handoff to sales is immediate and no lead sits in an inbox.
- 03
Wrote the education layer around the comparison the buyer is actually making — lease versus buying versus Florida PACE — rather than around the product being sold.
- 04
Built an automated content pipeline that generates search-optimised articles and publishes on a fixed schedule, so topical coverage compounds without an editor.
- 05
Structured the site for classic search, answer engines and generative results at once: question-shaped article titles, entity-consistent naming, and an llms.txt stating who the company is, what it sells, and where it operates.
What was built
- Next.js lead-generation site with an instant eligibility check
- CRM integration over an API for qualified submissions
- Automated article generation and scheduled publishing
- Comparison content covering lease, purchase and Florida PACE
- SEO, AEO and GEO layer including structured data and llms.txt
Stack
- Next.js
- CRM API integration
- Automated content pipeline
- Schema.org
- llms.txt
Outcome
Leads reach the CRM without manual handling, and the site publishes on a standing schedule with no editor in the loop — accumulating topical coverage designed to be found by search engines and cited by answer and generative engines.
In short
Can automated content actually rank, or does it just get penalised?
What gets penalised is content with no reason to exist — pages generated to fill a keyword slot, saying nothing a reader could not get elsewhere. Automation is not the problem; indifference is. A pipeline earns its output by being pointed at real questions the buyer is asking, grounded in specifics the business actually knows, and held to a publishing rate the topic can support. In this case that means articles answering what a solar lease is, how it differs from Florida PACE, whether a given roof qualifies, and what happens in a hurricane — questions with genuine answers, written against one consistent set of company facts. The same discipline is what makes the content citable by an answer engine: a page that resolves a question cleanly gets quoted, and a page that pads around a keyword does not.