For real estate

    Your buyers are asking AI before they call you.

    They ask about neighbourhoods, prices and risk long before they contact an agent. Whether your listings are part of that answer depends on whether an AI can read them — and most listing data cannot be read at all.

    • Built Parcela Nova
    • 340 municipalities scored
    • Diagnosis in days

    Where the hours go

    The same three every time.

    Three problems show up in almost every brokerage I talk to.

    Leads go cold in the gap

    An enquiry at 9pm gets answered at 10am. By then they have contacted three other agents. The fix is not a faster human — it is a system that responds instantly and qualifies before you spend time.

    Your listings are invisible to AI

    Price, size, location and features live inside page markup an AI crawler never renders. Ask an assistant about property in your area and you are not in the answer, however good your SEO was.

    The area knowledge is in your head

    Everything that makes you worth hiring — which streets flood, which schools matter, where the new road is going — exists nowhere a machine can reach. That is a moat you cannot use.

    What actually works

    Ranked by what pays back first.

    1 · Instant enquiry response and qualification

    Answers at any hour, asks the qualifying questions you would ask, books the viewing. The highest-return automation in this vertical, and usually live in weeks.

    2 · Follow-up that does not depend on memory

    Most lost deals were never rejected — they were forgotten. Sequenced follow-up recovers a surprising share of them.

    3 · Structured, agent-readable listing data

    Schema on every listing plus a queryable feed, so AI systems can cite specific properties instead of skipping you.

    4 · Your area knowledge, made queryable

    Turning local expertise into structured data an assistant can answer from. This is the Parcela Nova pattern at brokerage scale.

    The proof

    I have built this at country scale.

    Parcela Nova scores all 340 municipalities in Guatemala — buildable land, hazard exposure, electrification, drive time to the capital — from 12+ official sources and 10+ global datasets, and exposes the whole thing through an open MCP server any AI client can query.

    It is the same problem your brokerage has, at national scale: property information that existed but was unreadable, turned into something a machine can answer from.

    Read how it was built

    Questions

    The ones I actually get asked

    Is your inventory readable?

    Send me a listing URL. I'll tell you on the call exactly what an AI crawler sees when it looks at it — usually less than you would expect.

    Book a 20-min call