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Customer story · 2 min read

How an industrial developer replaced its leasing tracker in a weekend

Andrew Brown · Jul 13, 2026

Quick answer

An industrial developer's 11MB Excel tracker seeded a live system of record in one weekend — properties, buildings, bays, tenants, and leases imported, not re-keyed. AI arrived second, as a proposer: broker packets and texted updates parse into a review queue where a person accepts, maps, and promotes. Record first is why the AI stuck.

A Central Florida industrial developer ran a multi-park portfolio the way most do: an Excel leasing tracker as the source of truth, broker updates arriving by email and text, and a leasing meeting spent reconciling versions. This is how that spreadsheet became a governed system of record in a weekend — and why the AI came second.

What was actually in the spreadsheet?

Everything, which is the point. The workbook — 11MB of it — held properties, buildings, bay-by-bay square footage, tenants, lease dates and rates, and the pipeline. It wasn't bad data; it was good data in a fragile container. So instead of asking the team to re-key their world into a CRM, the tracker itself seeded the system: every property, building, bay, tenant, and lease imported as live records.

What changed for the team in week one?

  • The bay map replaced the grid. Vacancy, prospect, and leased states visible per building, with rollups computed from the bays — so the numbers can't disagree with the map.
  • Self-service edits. The team adds buildings, renumbers bays, and updates leases directly; every change is attributed.
  • Expirations became facts. Lease-end dates now surface on a dashboard feed, ranked by urgency, instead of living in someone's memory.

Where did the AI come in?

Only after the record existed — and only as a proposer:

  • Broker packets: the biweekly xlsx/PDF reports get read by the system into suggested rows (prospects, tours, comps), staged in a review queue. A person maps, accepts, promotes. Processing an 11MB packet takes about a minute; nothing goes live without approval.
  • Text-message capture: brokers text updates to a number; each text is parsed into the same review queue. ("Acme toured Building 3, needs 20k SF" becomes a structured prospect suggestion.)
  • Quick notes: the same capture box sits on the dashboard for thoughts on the go.

One review queue, one rule — propose, never commit — across every channel.

Why does the order matter?

Because AI output needs somewhere structured to land. Firms that pilot AI without a system of record get summaries beside the workflow; firms that fix the record first get suggestions inside it. The weekend was spent on the boring part — the import, the data model, the review queue — and that's precisely why the impressive parts stuck.

The team still works the way it worked. The difference is that the tracker now has memory, concurrency, and a review queue — and the spreadsheet is retired with honor.

FAQ

How do you migrate a leasing tracker spreadsheet into a CRM?
Import it — don't re-key it. The tracker already holds the portfolio truth: properties, buildings, suite-level square footage, tenants, lease terms. Seeding the system from the workbook preserves the team's trust in the data and makes the cutover days, not months.
What does AI actually do in this developer's system?
It reads: biweekly broker packets (xlsx/PDF) and texted broker updates get parsed into suggested rows — prospects, tours, comps — staged in a review queue. Humans map, accept, and promote. The model never writes to live records on its own.
Why build a custom system instead of buying a CRE platform?
Sub-institutional portfolios sit in a gap: enterprise leasing platforms are heavy and generic CRMs don't model bays and leases. A purpose-built system seeded from the team's own tracker fits the operation exactly and ships in days — the middle path between buy-too-big and build-too-slow.
What is a propose-never-commit review queue?
The governance pattern where every AI-extracted update — from a packet, a text, or a note — lands as a staged suggestion for human review instead of writing to records directly. Accepted items promote into the pipeline with attribution; rejected ones leave no residue. Trust comes from the workflow, not the model.

This system exists. The next one could be yours.

The build in this story runs a real firm today. Bring the workflow that hurts and we'll show you what the same approach looks like for your business.