What's Next

The build path from working engine to the thing that beats the incumbents

Sequenced to earn while it's built: brokerage cash first, then the learning loop that no incumbent can copy without rebuilding their stack, then AI qualification that lets us sell exclusive at a premium.

Now · shipped

Shipped

The engine + scoreboard

The internal dashboard is live: leads → sources → buyers → dispositions, with the metric incumbents ignore (cost per sold job) front and center. Lead intake, scoring, 30-day dedupe and UTM attribution all run end to end.

Full data model + metric views in Supabase
POST /api/leads intake — scored, deduped, attributed
Overview, Leads, Buyers, Sources, Intel screens

Week 1 · cash

Up next

Brokerage — revenue with zero build

Plug Kinsey's buyers into her buy-side sources (Adopt-a-Contractor, Intent Labs) and take the 35–50% spread. The engine already tracks source cost vs. resale price per lead, so margin is visible from lead one.

Onboard first 3–5 buyers by zip coverage + vertical
Wire buy-side source feeds into intake
Auto margin ledger per lead / per buyer

Weeks 2–4 · moat

Up next

Sale-routing + the learning loop

Route each lead exclusive-first, deduping against every buyer's 30-day window so we stop eating rejections. Then ingest disposition data (set → issue → demo → sold) and feed it back into targeting.

Exclusive-first routing engine (kills the 60% social reject problem)
Disposition webhook — buyers report outcomes back
Quality model retrains on what actually closes

Month 2 · qualify

On deck

AI qualification (Quo)

An AI voice agent qualifies every lead before sale against each buyer's 'issue-ready' criteria — turning $65 non-exclusive leads into $125 exclusive, provably-qualified ones.

Quo call-qualification on inbound leads
Buyer-specific qualification rubrics
Warm-transfer for emergency verticals (HVAC / plumbing)

Month 3 · scale

On deck

Buyer portal + generation at volume

Self-serve buyer portal delivering the 'zip file with perfect attribution' buyers beg for. Scale owned Meta generation across states and verticals, with weather-triggered storm campaigns.

Buyer self-serve dashboard + lead export
Owned Meta generation across TX / FL / CA
Weather-signal storm campaigns for roofing / window