Case study · Built in-house

Re-Engaging Cold Homeowner Conversations Without Losing the Personal Touch

Turning years of cold text threads — missed inspections, unanswered proposals, past clients — into a paced, owner-approved re-engagement program that sends from the owner's own number, inside the original thread.

Company
Certified Contracting LLC
Industry
Roofing
Status
Live: Running in production for a real business today.
Published
September 22, 2026

Verification pending. This write-up describes what was built and how it works. It contains no performance figures, and it will be updated with measured results once they are verified.

Challenge

A contracting company collects hundreds of text conversations that simply stop: an inspection that never got scheduled, a proposal that never got an answer, a happy customer nobody asked for a referral. Each one is a warm relationship going cold.

Existing workflow

Nothing — which is the usual answer. Following up on old threads by hand is exactly the work that loses to whatever is urgent today.

What we built

  • Targeting over an analyzed export of past conversations: segments (missed inspection, open proposal, past client), exclusions (opt-outs, group chats, live conversations, anything hostile) and a priority score.
  • Drafting with a language model that writes two or three variants in the owner’s own voice, grounded in that specific thread.
  • An approval queue — nothing sends until the owner approves it.
  • Paced sending from the owner’s own number inside the existing thread: business hours only, a daily cap, and a maximum of three touches per person.
  • A reply watcher that stops the sequence the moment someone answers and flags the thread as hot.

Human involvement

Approval is on by default. Warranty issues and unhappy-customer threads are never messaged automatically; they become tasks for the owner.

Result

The program is running for Certified Contracting and homeowners are replying. We are not publishing rates from a pilot this small; we will add measured numbers when the sample is large enough to mean something.

Lessons learned

  • Exclusions matter more than targeting. The most important work was deciding who not to message.
  • The thread is the context. Messages grounded in the actual last conversation read as personal because they are.
  • Personal-number sending has limits. Sending from a personal account carries spam-filtering risk, so volume is capped deliberately.
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