Context
A private equity firm with a thesis around rolling up fragmented property-services verticals — pavement striping, EV charging installation, pool maintenance, and several adjacent categories. Each of the portfolio companies operated locally or regionally, sold into commercial property owners and operators, and had been growing the way most property-services businesses grow: relationships, referrals, and the occasional purchased list of cold-call targets.
The PE firm’s value-add thesis depended on running these portcos faster and more efficiently than they’d been run as standalone businesses. Lead generation was one of the most acute bottlenecks. Without a step-change in how each portco found and qualified prospects, the rollup math didn’t work.
The challenge
Property-services lead generation sits at an awkward intersection:
- The total addressable market is enormous and visible. Every commercial parking lot needs striping. Every rooftop is a potential solar-or-EV-charging install. Every pool needs maintenance. The properties are not hidden — they sit in plain view, recorded in county records, owned by entities you can identify.
- But the qualification is hard. Which parking lots actually need re-striping right now? Which ones were done six months ago? Which rooftops are large enough to be worth a sales call? Which pools are commercial vs. residential? You can’t tell without looking at each one.
- And looking at each one is impossibly expensive. The traditional answer was either field reps driving routes, or buying lists from a data vendor and hoping. Neither produced enough qualified leads to support the growth rate the PE deal thesis required.
We needed a way to look at every property in a market, evaluate each one for service need, and route the qualified ones into outbound — at a cost low enough that it could run continuously, not as a one-time campaign.
The approach
We built a satellite-imagery-driven lead generation platform that runs on a per-vertical basis and gets deployed across each portco in the rollup.
Imagery acquisition. We pull commercial-grade satellite imagery for each market in scope, refreshed on a quarterly cadence. Each image covers a defined geographic tile down to the resolution required to evaluate the relevant service.
Property identification and enrichment. Every commercial property in the imagery gets identified, geocoded, and matched against public records — owner identity, owner contact info where available, building footprint, parcel size, prior sales, ownership entity structure.
Vertical-specific scoring. This is the part that makes or breaks the platform. For each vertical (pavement striping, EV charging, pool maintenance), we built a vision-driven scoring agent that evaluates each property against vertical-specific criteria: lot condition, paint visibility, existing infrastructure, lot size, traffic patterns, comparable nearby properties. The scoring engine produces a prioritized list — not “every commercial property,” but “the 200 properties in this market that most likely need this service today.”
Routing into outbound. Scored, prioritized leads route into an outbound automation platform — multi-touch email sequences, LinkedIn outreach, prepared call lists for inside sales — with messaging tuned to the vertical and the inferred property context.
Inside the system
The platform runs as a coordinated set of components:
- The imagery pipeline. Pulls satellite imagery for each active market on the refresh cadence, slices into per-property tiles, and queues for scoring.
- The scoring engine. Per-vertical agents evaluate each property tile and produce a structured score with a confidence rating and a written rationale. Properties below a confidence threshold get queued for human review; the rest move into the lead pool.
- The enrichment layer. Every scored lead gets matched to owner contact information from a combination of public records, commercial data providers, and inference. Leads without identifiable contact paths get dropped or sent to a research queue.
- The outbound engine. Qualified leads get sequenced into the right outbound playbook — email cadences, LinkedIn touches, call lists for inside sales — with messaging that references the specific property context surfaced by the scoring engine.
- The reporting layer. Each portco GM and the PE deal team see a real-time dashboard of pipeline coverage, scoring accuracy, and conversion to opportunity.
How the OS multiplies the investment
The platform is, in PE terms, a force multiplier across the rollup. Once the core OS exists, deploying it to a new portco — or extending it to a new vertical — is a matter of weeks, not months: the imagery pipeline, scoring framework, enrichment layer, and outbound engine all stay the same. What changes is the vertical-specific scoring criteria, the messaging, and the per-portco CRM integration.
That’s the standardization story the PE thesis depended on. Every new portco inherits the platform. Every new market for an existing portco lights up automatically. Every quarter of refreshed imagery sharpens the scoring further. The investment in the OS pays back across the entire portfolio, not just the portco that funded the build.