NewExtend and pREITend: The maturity wall in the filings, and the signal that flags who sells nextRead more

Acquire CRE with Conviction

Your firm’s understanding, put to work

AI in commercial real estate

The shift already started.

The difference shows up in what everyone else misses: the deal you never saw, the risk you caught late, the bid you lost. By the time the gap is visible in returns, it has been compounding for years.

Document Intelligence in CRE · A Rough Timeline
The adoption curve, roughly datedWhat a team can know →201920212023202520262027Manual ReviewAnalyst-hours · file storage systemsChat AssistantsSingle-document Q&ACopilotsPer-document retrievalCurated AI WorkflowsMemos without firm memoryContext-NativeFirm memory built into every deal

Most firms sit between chat assistants and copilots. The steep part of the curve is just starting.

Where Firms Sit Today
Share of institutional CRE firms · directional estimates
Manual Review
Analyst-hours · file storage systems
~36%
Chat Assistants
Single-document Q&A
~39%
Copilots
Per-document retrieval
~17%
Curated AI Workflows
Memos without firm memory
~7%
Context-Native
Firm memory built into every deal
The dashed segment: the next ~3%, making the jump within 6 months. The firms the rest will be chasing.
~1%

The sliver at the bottom is context-native. The dashed segment beside it is who joins them next.

Having AI is no longer the differentiator. The differentiator is whether that AI knows your firm.

Where the misses come from

Your team already runs deals through AI. It still doesn't know your deals.

01

No citations

The model can extract a number, but it can't point to the page it came from. Every figure gets re-verified by hand before the memo, and the bid goes in days later than it should.

02

No memory of your firm

The tenant that went dark on a space in 2023 shows up on a new deal's rent roll, and nothing flags it. The risk surfaces after you buy the building.

03

No market understanding

The pro forma says renewals at $1,450. Your own record in that submarket says $1,275. The question never gets asked, because the model has never seen your hundred rent rolls.

An underwriter thinks. The model summarizes. No prompt fixes that. Read the full article

Meet Starboard

A context engine built for commercial real estate investors.

Starboard takes every OM, rent roll, T-12, model, and memo your firm has ever touched, structures it, and reads every new deal against all of it.

How it works

Three steps between a data room and conviction.

01

Extract

Every document is broken down into the fields that matter: rents, tenants, lease terms, formulas.

02

Connect

A tenant on a new rent roll links to every lease of theirs your firm has ever seen.

03

Cite

Every fact keeps a link back to the document, the page, the cell it came from.

Deals
24
Connections
18
ABC IndustrialTenantConcorde, MASubmarketOstermere CapitalLenderParish PartnersCo-GPPhase I ESAEnviro riskDavid JonesBroker
DealsnameThe people & terms they share. Click one to light up its deals.

Ten years of deal history, working on the deal in front of you. See the full pipeline on the product page

app.starboard-ai.com
Your deal data stays yours. It never trains models.

Purpose-built for CRE underwriting, deployed to institutional security standards. SOC 2 Type II.

How we handle your data →

Better investors, or just faster ones?

Send us one deal you're evaluating this week. We'll show you what Starboard does with it.

SOC 2 Type II. Your deal data never trains models. Trust center