You're running 11 million square feet of commercial real estate with a team of roughly 20 people — and you're doing it at a level that earns CoreNet recognition, Crain's coverage, and CoStar Power Broker status. That kind of output requires exceptional people working exceptionally hard. It also means the manual workload is real: hundreds of leases tracked by hand, investment models rebuilt from scratch on every deal, and one person carrying the full weight of marketing for a 50-client firm.
The good news: you're not starting from zero. SAM is already part of how your team works. You've seen what AI can do in a general sense — what's missing is a version of that power built specifically around your workflows, your document types, and your clients.
Beyond AI's role here isn't to introduce artificial intelligence to Team CORE. It's to accelerate what you've already started — turning the tools you're already reaching for into systems that run the repetitive work for you, so your team can focus on the relationships and decisions that actually require a human.
You raised an important question about keeping your data secure when using AI. It's the right question. Everything we build together runs on private, secure infrastructure — your data never touches a public model. That's not a premium add-on; it's how we work.
Hundreds of leases across 11M+ SF — each with its own rent escalations, TI deadlines, options, insurance requirements, and expiration dates that all need to be tracked manually. A missed notice or overlooked clause isn't a paperwork issue; it's a client liability.
Daily OperationsEvery deal starts with a blank NOI model, a manual cap rate build, and an amortization schedule assembled from scratch. Anomaly detection — unusual rent rolls, unverified income streams — requires a careful human read every time. Comp analysis is time-consuming and rarely lands in the same format twice.
Revenue-CriticalLease abstracts, offering memorandums, and letters of intent follow a predictable structure on every deal — yet each one still requires hours of drafting, formatting, and cross-checking. Template consistency breaks down when deal details change mid-process, creating rework loops that eat into billable time.
Daily OperationsTurning raw portfolio data into polished Word, Excel, and PowerPoint deliverables — like the recurring reports produced for clients such as Rehmann — is a production bottleneck every single cycle. The data exists; the hours are lost formatting it into something presentable.
Client-FacingOne person manages all marketing for a firm with 50+ corporate clients — new business outreach, drip campaigns, social presence, and brand consistency. The "Team" culture and client-first reputation deserve more consistent touchpoints than a single-person bandwidth allows.
Client-FacingBuilding annual property budgets requires pulling multiple years of P&L data, weighting recent years more heavily, and reconciling actuals against projections — all done manually, from scratch, every cycle. It's structured, predictable work that shouldn't require this much time.
Daily OperationsStaying current on tenant activity, lease expirations, submarket trends, and competitive transactions across Michigan means pulling from CoStar, internal records, and external sources simultaneously — then synthesizing it all into a coherent picture. There's no streamlined workflow to make that fast or consistent.
Strategic| Pain Point | Priority | Current Pain | What AI Does | Complexity |
|---|---|---|---|---|
|
01
Lease Abstraction & Management
|
Top Priority | Hundreds of leases tracked manually with no automated alerts for critical dates, rent steps, or tenant obligations. |
Upload a lease PDF → instant structured abstract with key terms, dates, and auto-scheduled expiration reminders at 18/12/6 months. |
Moderate
|
|
03
Document Production
|
Quick Win | OMs, LOIs, and lease abstracts follow the same structure every deal yet still require hours of manual drafting and formatting. |
Input deal data → AI generates a polished, consistently formatted draft document in minutes, ready for review. |
Simple
|
|
04
Client Reporting
|
Quick Win | Recurring deliverables (like Rehmann reports) require manual data pulls, formatting, and layout work every reporting cycle. |
Raw data in → polished report out, with consistent formatting, automated narrative summaries, and zero manual layout work. |
Simple
|
|
05
Marketing & Client Engagement
|
Quick Win | One person managing all marketing for a 50+ client firm means outreach is inconsistent and campaigns lack the cadence they deserve. |
AI drafts drip sequences, client touchpoint emails, and social content from brief prompts — multiplying one person's output severalfold. |
Simple
|
|
02
Investment Analysis Reports
|
Major Project | NOI, cap rate, and amortization models are rebuilt from scratch on every deal, with no automated anomaly detection on rent rolls. |
Input deal financials → AI generates a fully structured underwriting report with flagged anomalies and comparable transaction context. |
Complex
|
|
06
Property Budgeting
|
Later Phase | Annual property budgets are assembled manually from multiple years of P&L data, with recency-weighting applied by hand each cycle. |
Upload historical P&L → AI builds a weighted budget with variance analysis and year-over-year commentary, ready in minutes. |
Moderate
|
|
07
Market & Portfolio Research
|
Later Phase | Synthesizing tenant activity, lease expirations, and submarket trends across Michigan requires pulling from multiple sources with no unified workflow. |
AI agent monitors defined data sources, surfaces relevant activity daily, and delivers structured research briefs on demand. |
Complex
|