Case study / Enterprise · Commercial Real Estate

Surfacing Placement Opportunities for CBRE's Leasing Division

Commercial real estate is a market where placement timing decides the deal. The ingestion layer doesn't replace the broker's judgment. It gives the team a starting line that's hours ahead of where they used to begin. Tenant signals, market activity, and available inventory land in one place on a cadence the team can plan around, so the conversations that turn into placements happen while the opportunity is still open. Pulling external market signals into the same surface also gives the team market context for placement decisions they previously had to make from internal data alone.

The result300+

data sources unified

300+ data sources unified into a single working surface for the leasing division
Weekly manual rebuilds replaced with scheduled runs that complete before the team logs in Monday
Placement opportunities surface earlier in the week, while the fit is still in play
Single source of truth across the team eliminated reconciliation between analysts' working copies

01 / Before and after

The same work, two ways.

The manual wayWith Bell AI

Pull from each of 300+ tenant, market, and inventory sources by hand at the start of the week.

Open the working surface on Monday with the full market already loaded.

Reconcile differences across analysts' working copies before any market view is trustworthy.

Trust monitoring to flag any source that failed overnight.

Race to finish ingestion while competitors are already quoting on fits the team can't yet see.

See new fits between prospective clients and leasable offices the moment they surface.

Reach Friday with most of the week spent on plumbing instead of placement work.

Spend the week winning placements while the opportunity is still in play.

Before~90 min

Pre-meeting sanity checks before every client conversation

After~15 min

Source-cited data audit with figures ready to defend

02 / The work

Where it stuck. What we built.

The challenge

In leasing, the best placement isn't the one you find. It's the one you find first. CBRE's leasing division was working across 300+ data sources covering tenant signals, market activity, and available office inventory, but the picture was fragmented. By the time the team had rebuilt the underlying datasets each week, fits between prospective clients and leasable offices had already been quoted by a competitor. The bottleneck wasn't talent. It was the time it took to assemble the market view.

The solution

We built an automated ingestion layer that pulls from every source on a schedule, normalizes the data, and lands it in the team's working surface before Monday morning. Instead of rebuilding the same datasets each week, the team opens the week with the full market already loaded, and the moment a new fit between a prospective client and a leasable office shows up, it's visible to the people who can act on it. Every figure in the working surface cites the source it came from, so an analyst can defend a number in a client meeting instead of going to find it after.

03 / How we built it

Step by step.

Each step was tested against the team's real work before the next one started. A person reviews anything that matters before it goes out.

01Step 1 of 4

Mapped 300+ data sources across tenant, market, and inventory feeds in scope for the leasing team

02Step 2 of 4

Built ingestion connectors for each source with normalization at write time so downstream views stay clean

03Step 3 of 4

Wired the unified output into the team's working surface so the market is open in one place each Monday

04Step 4 of 4

Added scheduling and monitoring so silent failures and stale sources surface immediately, not days later

Tools

Live Market Data
Airtable
Python
Claude
RSS Feeds
Vercel

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