ChatGPT Alternatives for Commercial Real Estate Teams

Strengths

• The terminal reads every document on the deal. One instruction can take those files through the model to a memo ready for review.
• Source-traced extraction. The rent roll comes out unit by unit, every figure tied to the exact file, sheet, and row it came from and footed to the document’s own totals. The T-12 lands on a standard expense set with an NOI bridge.
• Build an Excel model with live formulas in your template or an institutional format. Switch scenarios and keep Word memos and decks on the same record. Inspect or undo terminal revisions in the open workbook. Memo changes are tracked under your name. Formulas and charts stay intact when the file opens in Excel.
• Two analysts and the terminal can edit the same sheet. Facts, parties, and decisions carry to later sessions. Memos draw from the model in your firm’s voice. Each phase ends with a locked snapshot. Every firm has its own database and storage.

Trade-offs

• The deal’s own documents are the source. Drop them on the deal, or link the SharePoint, OneDrive, Dropbox, or Box folder where they already sit and file the broker’s attachments from Outlook. A linked folder is the edge of what the terminal can see. The firm’s market-data and comp subscriptions sit alongside it.
• It runs the deal work and pairs with the pipeline tool the firm already keeps, so contact and broker-relationship tracking and the deal-flow funnel stay where the firm runs them today.
• No self-serve checkout. Pro is for funds and deal teams of up to 50 people, up and running with live deals within 24 hours. Enterprise deploys into the firm’s own AWS account, with single sign-on and customer-held keys. The way in is a working session on one live deal.

Strengths

• Ten ready-to-run agent templates spanning pitch building, earnings review, valuation review, reconciliation, and KYC screening, with production customers including JPMorganChase, Goldman Sachs, and Citibank.
• Pre-built connections to FactSet, S&P Capital IQ, MSCI, PitchBook, Morningstar, Moody’s, LSEG, and other market data and research providers.
• Microsoft 365 add-ins for Excel, PowerPoint, and Word, with context carrying across the applications in a single session, and no training on customer data by default.

Trade-offs

• No CRE workflow anywhere in its materials: nothing describes rent roll ingestion, T-12 normalization, or property-level underwriting, and the model builder works from filings and data feeds, not operating statements.
• No deal lifecycle: no stages from screening to closing, no per-deal workspace, no memory of the deal between sessions; it is a finance assistant with templates, not a deal record.
• The connector roster carries no CRE data providers, and no per-customer isolated deployment is described in its public materials.

Strengths

• Included in every paid Claude plan, from $20 a month for an individual, with no add-on fee.
• Capable desktop work: file organization, document drafting with citations back to source files, scheduled recurring tasks, and parallel workstreams running at once.
• Optional financial services plugins add DCF, comps, and investment-memo commands, and customer data stays out of model training by default on paid plans.

Trade-offs

• Horizontal by design: it does not know what a rent roll is; the financial services plugins cover corporate verticals like banking and equity research, and no CRE underwriting workflow appears among them.
• Single-user and session-bound: the desktop must stay open for work to run, and there is no shared deal workspace with versioned outputs for a team.
• A personal productivity agent on one person’s machine, not a firm’s deal platform: the investment-memo command targets generic corporate and private equity committees, and nothing in its materials describes CRE memo conventions, a firm’s own isolated environment, or a deal carried across sessions.

Strengths

• It lives where the work already happens: multi-step actions in Word, Excel, and PowerPoint reached general availability in April 2026, and plain-language prompts can now sit inside spreadsheet cells as a worksheet function.
• Answers ground in the firm’s own organizational data under the permissions each user already holds, with no training on customer data and a long compliance roster behind it.
• Plain economics: $30 per user per month on the enterprise tier, added to the Microsoft 365 plan the firm already pays for.

Trade-offs

• No CRE capability: no rent roll or T-12 ingestion, no underwriting model, no memo in the house voice, and its Finance Agent binds to ERP systems like Dynamics 365 and SAP that CRE investment firms rarely run.
• Reliability and memory gaps a deal team will feel: a widely reported September 2025 incident showed Copilot in Excel failing basic addition, and session context is purged when the session ends.
• It requires a qualifying Microsoft 365 subscription underneath, and Copilot in Excel works only on files saved to OneDrive or SharePoint with AutoSave on.

Strengths

• Bundled into paid Workspace plans since January 2025, so a Google firm gets it without a new purchase decision or a new vendor review.
• Useful suite work: Sheets builds formulas from plain language, NotebookLM synthesizes across documents and keeps sources inside the organization, and Meet writes the meeting notes.
• Customer Workspace data is not used to train models outside the customer’s own domain without permission.

Trade-offs

• No native Excel: Gemini cannot create or edit native Excel formulas, so a firm with Excel-based models cannot use it to extend them; the work happens in Sheets or not at all.
• Google’s own benchmark for complex spreadsheet generation in Sheets sits at just over 70 percent, and there is no concept of a deal, an underwrite, or a committee anywhere in the product.
• Usage caps bite on an active team: a 500-use monthly limit on writing help and up to 100 prompts a day on some plans.

Strengths

• Speed: in-app parsing in under a minute into the firm’s own Excel model or Archer’s pre-built multifamily template, and an NOI estimate from an address alone in under two minutes.
• A comp cloud of more than 150,000 properties, with rent and expense comps and seller intelligence.
• A pre-built multifamily model with dual loan structures, renovation scheduling, and waterfall analysis, plus pipeline tracking, with unlimited users on every plan.

Trade-offs

• Multifamily is the deep focus; other asset classes were added for market research, and full underwriting depth outside multifamily is not documented.
• No narrative work: it does not draft IC or credit memos in the firm’s voice; the committee-ready output is a one-page model tab, not prose.
• The lifecycle ends early: no closing deliverables, no documented asset-management tracking against the underwrite, and no dedicated per-firm deployment described in its materials.