AI for CRE Loan Term Sheet Grids: 4 Options Compared

By Jude Lee · October 16, 2026 · Comparison

Two CRE capital markets brokers comparing printed lender term sheets at a conference table with a laptop spreadsheet open

Why term sheets are the right first agentic project

Capital markets work keeps drawing attention from AI vendors because it has the three traits automation likes: it is document-dense, highly repetitive in structure, and deadline-driven. Term sheet comparison adds a fourth trait that matters more than the other three — the output is checkable. Every field in a quote grid traces back to a specific line in a specific PDF, so a reviewer can audit the machine in minutes rather than trusting it.

That is the opposite of “AI underwriting,” where the output is a judgment you can’t verify line by line. For a debt brokerage or an investment sales team placing financing, the nearest high-value target isn’t the underwrite. It’s the quote grid.

The actual job: eight PDFs, one comparable grid

A competitive assignment generates term sheets from a mix of banks, debt funds, life companies, and agency lenders. Each writes in its own house format. You need a single sheet showing, per quote: loan amount and the LTV/LTC it implies, rate basis (fixed coupon vs. index + spread, with any floor), term and extension options plus their conditions, amortization and IO period, the sizing constraint that actually bound the proceeds (DSCR, debt yield, or LTV), recourse and any burn-off, prepayment structure (yield maintenance, defeasance, step-down, open period), origination and exit fees, reserves and holdbacks, earnout mechanics, third-party deposit, and quote expiration.

That’s not summarization. It’s normalization — turning “L+275 with a 3.50% floor” and “6.15% fixed, 5-year” into numbers a client can rank, with the assumptions stated.

Comparing term sheets is a normalization problem, not a reading problem — and normalization needs explicit rules, not vibes.

Option 1: an associate and a spreadsheet template

Still the default, and still defensible. A good analyst with a locked template produces a grid you can defend in front of a client and a credit committee. The cost is cycle time and fragility: the template lives on one laptop, the second round of revised quotes arrives at 6pm, and the person who knows the formulas is on a plane.

Keep this option when your deal volume is low, every assignment is structurally weird, or you simply don’t have clean digital copies of what lenders send.

Option 2: AI features inside lending and deal platforms

Debt marketplaces, loan origination systems, and CRE deal platforms increasingly ship document-extraction and summarization features. The appeal is obvious: no build, vendor-maintained, support line, and the output lands where your pipeline already lives.

The honest limits, as of 2026: these features are built for the vendor’s data model, not your grid. If your firm presents net effective proceeds after fees and a recourse-adjusted spread, and the platform presents rate/term/LTV, you’ll re-key anyway. Ask any vendor three questions before buying — can I define my own fields, can I export the structured output, and what happens to my documents and my client’s confidential financials. Verify data-handling answers in the product’s own documentation and your lender NDAs rather than taking a sales answer.

Keep this option when the documents already live in that platform, your grid is close to the vendor’s default fields, and nobody on the team wants to own a build — in that situation the vendor feature is genuinely the right call, and anything custom is overhead.

Option 3: a general assistant plus a written skill

This is the underrated middle. A skill is a packaged, reusable instruction set that teaches an assistant like Claude or ChatGPT to do one job the same way every time — in this case: extract every field in your schema from a term sheet, express rate as both all-in coupon and index+spread, flag the binding sizing constraint, output JSON in that schema, and write “NOT STATED” rather than inferring anything.

Written once, that skill makes the output consistent across your whole team instead of varying with whoever typed the prompt. Our broader take on packaging instructions this way is in standardizing agent skills for CRE. You still upload files by hand, and you still review every number — but you’ve removed the “everyone prompts differently” failure mode.

Option 4: a custom agent connected to your systems via MCP

MCP (Model Context Protocol) is an open standard for giving an AI assistant governed access to your tools and data. A custom MCP server over your deal folders, email, and CRM lets an agent run the whole loop: watch the financing mailbox, detect an inbound term sheet, extract the fields using your skill, write a row into the deal’s quote grid, flag what changed from the lender’s prior version, and post a summary to the deal channel for the broker to approve.

That’s a real multi-step agent, not a chatbot. It’s also the most work: you’re building and maintaining software. The mechanics of standing one up are covered in connecting Claude to your systems with a custom MCP server, and it pairs naturally with how you already assemble lender submission packages and run lender matching.

Skill on a general assistant

Typically days of drafting, depending on how settled your grid schema already is. No engineering; works on any PDF you upload; a human drives every run. Best when quotes arrive in bursts a few times a month and you mainly want consistency.

Custom agent + MCP server

Directionally weeks of build plus ongoing maintenance, assuming you have in-house or contracted engineering. Triggers itself from email and writes to your systems. Best when financing is a core line of business and the same grid gets rebuilt every week across many assignments.

How to think about payback without inventing numbers

Don’t trust anyone’s published savings figure, including ours. Model it yourself:

hours × loaded rate

Baseline cost per grid — time your own next assignment

Your timesheet

grids per month

Multiply by real volume, not peak volume

Your pipeline

review time retained

Subtract — AI shifts work to checking, it doesn't delete it

Assumption you set

The larger value usually isn’t the saved hours; it’s speed to a revised grid when a lender re-trades, fewer transcription errors in a document a client makes a decision on, and analyst hours redirected to sourcing rather than re-keying. All three are worth something; none has a number we can honestly supply. For a fuller framework, see our CRE automation payback worksheet.

Which approach actually fits your desk

There’s no universal answer, and anyone giving one is selling something. Our opinion, plainly labeled as opinion: if you place debt occasionally, a written skill on a general assistant plus your existing Excel template wins on cost and speed to value. If debt placement is a core desk with weekly volume and multiple producers, a custom agent over your own data pays for itself in consistency. If your origination platform already holds the documents and your grid is simple, use the vendor feature and stop.

Two shortcuts that don’t apply to commercial debt

Brokers newer to the asset class often ask about the 2% rule — the residential rental screen where monthly rent should be roughly 2% of purchase price. It is not a commercial underwriting standard, and no lender will size a loan off it. Commercial debt is sized off DSCR, debt yield, and LTV/LTC, which is exactly why your grid should show which of the three bound the proceeds.

You’ll also see a “30% rule for AI” circulating. We’re not aware of any authoritative body that publishes such a rule, and we’d treat the phrase as internet shorthand rather than a standard. Don’t build policy on it. A more useful policy: define per workflow which outputs are draft-only, which require a named reviewer, and which an agent may send without a human. Our autonomy levels guide lays that out.

A two-week pilot

Freeze your grid schema

Write down every field, its units, and its definition. This artifact is the project — tooling is downstream of it.
Collect ten past term sheets

Span lender types. Include one ugly scan and one term sheet with an earnout.
Write the skill

Extraction only, explicit “NOT STATED” rule, JSON output matching your schema.
Run a blind accuracy check

Have an analyst build two grids by hand and compare field by field. Count misses by field, not overall — in our view failures tend to cluster in prepayment and extension conditions, so check there first.
Decide on scope, not on the demo

Only if volume justifies it, move to an MCP-connected agent that triggers from email. Otherwise keep the skill and bank the win.

Term sheets are a good first agentic project precisely because the output is checkable: every field traces to a line in a PDF. Start where you can verify the work, and let the scope earn its way up.

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