CASE NOTE / REALTOR LEADS

Relocation guides, lead capture, and local intelligence.

A Python platform that turns regional information, first-party behavior, and public-source signals into usable lead workflows.

01 / CONTEXT

The system connected content, data, and follow-up.

The build was not only a website. It connected static relocation guides, lead forms, scoring rules, analytics events, public records, and extracted signals into a portal that could support actual follow-up.

Domain
Regional real estate and relocation intelligence
Core
Guide sites, lead capture, scoring, public-source enrichment
Stack
Python, Flask, Postgres, Typer, static HTML/CSS/JS

02 / WHAT SHIPPED

A small intelligence system around a real sales motion.

01

Two static guide sites.

Publish relocation pages with maps, calculators, local resources, and fast static delivery for search and sharing.

02

Lead capture and scoring.

Capture first-party intent, normalize records, rank signals, and make the next action visible inside an authenticated portal.

03

Data pipeline and exports.

Use public sources and LLM extraction carefully, then resolve, review, score, and export data instead of leaving it in raw scraped form.

03 / PATTERN

The useful lesson: local knowledge becomes more valuable when it becomes operable.

This is the data-work shape Seam likes: take scattered public and first-party signals, build a usable workflow around them, and keep the final system plain enough that a small team can work it every week.