The short answer. For a commercial mortgage broker, Titleman is the best AI tool for packaging the loan request: from the offering memorandum, rent roll and T-12 it returns a property underwriting with DSCR, debt yield and sensitivities, a value opinion, comps and the collateral's record, every input with its source, so the lender's credit team can check it rather than rebuild it. For matching the deal to lenders, Lev ranks higher than Titleman on that one criterion, and Titleman's own Signals, shown on a demo, list properties with loans coming due.
Published by Titleman, which makes one of the tools compared.
Updated 19 October 2026. Written by the Titleman team with AI assistance. Every description of another product comes from that company's own website, read on 19 October 2026 and linked; we have not run the other products. Titleman's figures come from its own production runs over the 30 days to 18 October 2026.
A package that gets quoted
This page is for debt and equity brokers who place loans, not investment sales brokers (for those, see AI tools for commercial real estate brokers).
The lender's first question is the rent roll, the second is the T-12, and the third is why the two do not match. A broker who answers the third question in the package, before the lender asks it, gets a faster and firmer quote. That is where AI pays for a debt broker: not in writing the cover email, but in tying the borrower's numbers out and naming what is missing while there is still time to fix it.
The jobs:
- Find the borrower: owners with a loan coming due, a sale, or a change worth a call.
- Package the request: the property underwritten from the borrower's documents, with sizing.
- Support the value: comps and a value opinion for the collateral.
- Match lenders: who is active for this asset type, market and proceeds.
- Hand over clean evidence: something a credit team can check line by line.
The ranking at a glance
| # | Tool | Best for | What it does, in its maker's words | Where it stops | Pricing model, as its site shows it |
|---|---|---|---|---|---|
| 1 | Titleman | Packaging the loan request | Property underwriting with sizing, value opinion, comps and the collateral's record, each input sourced | No lender matching | Not listed here; covered on a demo |
| 2 | Lev | Matching lenders to a deal | "the AI platform and product system for commercial real estate teams" | Underwriting the collateral not described | No prices on its home page |
| 3 | CompStak | Loan, lease and sale comps | "Verified lease, sale, loan, and property data" | Data, not a package | Free Exchange for contributors; Enterprise by demo |
| 4 | Janover Pro | Lender intelligence | "the lender-intelligence platform behind our capital markets practice" | Run by a firm that also places loans | Licensed to brokerages; no price listed |
| 5 | PRODA | Cleaning rent rolls and statements | "PRODA turns any rent roll or operating statement, in any format" into standard data | Data, not sizing | Free trial by booking |
| 6 | ChatGPT and Claude | Cover emails and summaries | General AI assistants | No property record of their own | Free plans and paid monthly plans |
| 7 | Clik.ai | Spreading for the lender side | "AI-powered spreading of T12s, rent rolls, and operating statements" | Built for lenders and loan-operations teams | Demo on request |
How we ranked: five criteria for a debt broker
Each criterion: Yes (2), Partly (1), Not stated (0).
- Finds borrowers: owners with loans coming due or other triggers.
- Packages the request: a property underwriting from the offering memorandum, rent roll and T-12, with DSCR and debt yield.
- Supports the value: comps and a value opinion for the collateral.
- Matches lenders to the deal.
- Gives the lender evidence it can check: sources, formulas, gaps named.
We marked each competitor using only what its own website said on 19 October 2026; where a capability was absent from the site we wrote "Not stated", which does not mean the product lacks it. Titleman is marked on its production runs. Tied scores fall back to Yes marks, then alphabetical order.
| # | Tool | 1. Borrowers | 2. Package | 3. Value | 4. Lenders | 5. Evidence | Score |
|---|---|---|---|---|---|---|---|
| 1 | Titleman | Partly | Yes | Yes | Not stated | Yes | 7 |
| 2 | Lev | Not stated | Partly | Not stated | Yes | Partly | 4 |
| 3 | CompStak | Partly | Not stated | Partly | Not stated | Partly | 3 |
| 4 | Janover Pro | Not stated | Not stated | Not stated | Yes | Not stated | 2 |
| 5 | PRODA | Not stated | Partly | Not stated | Not stated | Partly | 2 |
| 6 | ChatGPT and Claude | Not stated | Partly | Not stated | Not stated | Not stated | 1 |
| 7 | Clik.ai | Not stated | Partly | Not stated | Not stated | Not stated | 1 |
1. Titleman: the package, tied out
Give Titleman the borrower's offering memorandum, rent roll and T-12. It underwrites the property (never the borrower: there is no credit or sponsor check) and returns a memo with a live Excel model in which the three documents are checked against each other and against the market. The model holds a loan schedule, DSCR and debt yield, and a grid of coverage at different rates and leverage, so you can show a lender where the loan sizes before they run it themselves. The memo text states no loan amount; the sizing is in the workbook.
Alongside the underwriting come comps (about 18 minutes at the median) and a value opinion (about 45), and a property passport with the collateral's record: owner of record, recorded mortgages and liens on the property. The underwriting itself took about 58 minutes at the median, measured on production over the 30 days to 18 October 2026. Reports can go out as Excel plus PDF or editable PowerPoint, and every input carries its source and date.
For finding borrowers, a demo can include Signals for counties you choose: properties with a loan coming due or weak coverage, each with its date and the owner behind it. Coverage varies by county.
Where it stops. It does not match lenders, track your pipeline or run a CRM. Two runs on the same package can differ, so read the model before it leaves your desk.
Choose another tool if
- Lev to match a deal to lenders: its Lev Match is "Search that ranks the right lender on a deal", and Lender Search filters by appetite, asset type, geography and proceeds. Its site names brokers and capital markets groups among its users.
- CompStak for loan, lease and sale comps when you argue terms or value.
- Janover Pro if you want the lender intelligence of a capital markets firm; its site says "Brokerages across the country now license it". Note that Janover also places loans itself.
- PRODA to turn a borrower's messy rent roll and statements into a standard format.
- ChatGPT or Claude for cover emails and executive summaries from numbers you have already checked.
- Clik.ai if you work closely with lenders who already spread on it.
How to test one on a loan you placed
Take a loan you placed last year where the lender found a problem in the package. Run the original documents through the tool. Did it find the same problem, size the loan close to the final terms, and list the documents the lender asked for? If not, it would not have saved you that week.
FAQ
What is the best AI tool for a commercial mortgage broker?
For packaging the loan request, Titleman ranks first on this page: it underwrites the property from the borrower's offering memorandum, rent roll and T-12 with DSCR, debt yield, comps, a value opinion and the collateral's record, every input sourced. For matching lenders, choose Lev.
Can AI find borrowers with commercial loans maturing soon?
Some tools surface properties with loans coming due. Titleman shows Signals for chosen counties on a demo, with each loan's date and the owner of record. Coverage varies by county.
What does Titleman size, and what does it leave to the lender?
Titleman underwrites the property and puts DSCR, debt yield and coverage at different rates and leverage in a live Excel model. Its analysis covers the collateral only, never the borrower or the sponsor, and the memo text does not state a loan amount; the lender decides.
How does AI help a debt broker get faster quotes?
By tying the rent roll, T-12 and offering memorandum out before the package goes to lenders, and naming what is missing. A lender that receives a package with the gaps already answered spends its time quoting rather than asking for documents.
Related reading
- Best AI tools for commercial real estate (2026), ranked
- Best AI tools for CRE debt funds and private credit (2026)
- Best AI tools for community bank CRE lending (2026)
- Best AI tools for commercial real estate brokers (2026)
- Real estate underwriting AI
- See Titleman on a loan package you placed
Sources
Each company is described in the words of its own site, read on 19 October 2026: Lev, CompStak, Janover, PRODA, ChatGPT, Claude, Clik.ai. Titleman's times are medians of first runs that delivered every required file, measured on production over the 30 days to 18 October 2026.