Model Match
The intelligence layer for mortgage & real estate data

Start with the question, not the report.

Model Match holds the mortgage and real estate data — loan officers, agents, lenders, borrowers, and markets — and it doesn't have to live in our platform. Connect it to Claude, ChatGPT, or whatever you already work in, and go straight from a question to an answer.

Included on every paid plan and during your trial · no enterprise contract, no waiting on us

Claude Model Match
Who are the top mortgage lenders in the Charlotte metro this year?
Pulling lender production for Charlotte, NC metro, last 12 months:
companyAnalytics · metro: Charlotte, NC 2.0s

Top mortgage lenders · Charlotte, NC metro · 2026

Lender Volume Share YoY
Beacon Lending Group $1.84B 9.2% +1.4 pt
Summit Mortgage $1.57B 7.8% +0.6 pt
Acme Home Loans $1.31B 6.5% −0.9 pt
Meridian Home Loans $1.06B 5.3% +0.3 pt
Company records Market data

Trusted by over 25,000 industry professionals:

Movement Mortgage Edge Home Finance PRMG Rocket Pro Barrett Financial Atlantic Coast MortgagePremier Mortgage Resources Union Home Mortgage Lower Finance of America Reverse

Getting the data was never the hard part.

Getting from the data to an answer was. Software has always made you learn how it organizes things before it would tell you anything. That's the part that changes — you start with the question, and let it work out which records it needs.

What answering this used to take
  1. 1 Work out which report even answers the question
  2. 2 Run it, export it, then export the second one you need to compare against
  3. 3 Reconcile both in a spreadsheet
  4. 4 Piece an answer together by hand
  5. 5 Start over the moment the question changes

Reports, exports, spreadsheets, and most of an afternoon.

What it takes now

“Where should I actually spend my next 90 days?”

No decision about which report to run. Just the question, then a few follow-ups. Minutes later you know:

  • Where they were already winning
  • Where they were underweight
  • Which agents in their markets were working with 25+ different loan officers

And you keep chipping away from there, until a broad question has turned into a plan.

Based on a working session with a loan officer; shown as an illustration of the flow.

Setup

Two minutes, and no IT ticket

You add Model Match the same way you'd add any other connector to your assistant — from its settings screen, with a URL and a sign-in.

Watch the setup (4 min)
  1. 1

    Start a Model Match trial

    The connection signs in as you, so it needs an account. Fourteen days free, no setup call, nothing to schedule.

  2. 2

    Add the connector

    In Claude or ChatGPT, open connectors and add Model Match. Paste one URL, sign in when prompted, and approve the connection. That's the whole setup.

  3. 3

    Ask it something you already know the answer to

    Best first move: ask about a market you know cold. When the names and volume match what you already know is true, you'll trust it on the markets you don't.

terminal
~ %

Added MCP server “modelmatch”

search_originators · market_signals · analyze_loans — ready in your assistant

~ %

Prefer the command line? Claude Code, Cursor, and any other MCP client take the server in one line — but you never have to open a terminal to use this.

Things you'd have opened a dashboard for

Type them the way you'd say them out loud. Your assistant works out which records it needs, and you can keep pulling the thread — every follow-up keeps the context of the last answer.

Agent partners

  • “Which buyer's agents in my county closed the most purchase deals last quarter?”
  • “Who does that agent currently send her loans to?”
  • “Find agents near my office working the $400–600K range.”

Your own book

  • “Pull my funded production for the last two years by loan type.”
  • “Which of my past borrowers are still in the home?”
  • “How did my volume trend against my market?”

The competition

  • “Which lenders are gaining share in my metro this year?”
  • “Who's writing the FHA volume in these three ZIPs?”
  • “Show me the top 10 originators at that shop.”

The market

  • “What's the purchase-to-refi mix in my county right now?”
  • “How much volume was written in Ada County last quarter?”
  • “Give me a market snapshot I can put in a listing presentation.”

No walled garden

The value doesn't have to live in our platform

Chat is one way in, not the only one. The same data reaches your AI tools, your own applications, and your integrations — and none of it requires an enterprise contract, a sales conversation, or our permission to start.

In your AI tools

Claude, ChatGPT, Claude Code, Cursor — anything that speaks MCP. Ask in plain English and it queries the records directly.

How the MCP server works

In apps you build

A complete API across virtually every segment of the data, with public documentation and sample apps you can copy and deploy.

Developer docs

In your own integrations

A self-serve OAuth client builder, so you can create and ship an integration into your stack without waiting on us to build it for you.

In the platform

And when you'd rather just log in and work — search, market reports, and the CRM are all still right there.

Some people live in Model Match every day. Some pull the data into another system and barely open the interface. Some build something we'd never have thought to build. We're good with all of it — the goal is to be the most useful, not the only place you can work.

Keep pulling the thread

Follow up without rebuilding the search

The part a dashboard can't do. Narrow it to one county, drop the refis, sort it the other way — each follow-up carries the last answer's context, so you're refining a conversation instead of starting a filter over.

Ask Model Match Share
Who are the top buyer agents in Mecklenburg County this year?

Top buyer agents · Mecklenburg County, 2026

1 Priya Nair 38 buy-side units · $14.2M
2 Marcus Delgado 31 buy-side units · $11.8M
3 Hannah Brooks 27 buy-side units · $9.6M
Just the ones doing mostly FHA deals
Keeping your Mecklenburg County + 2026 filters, narrowing by loan type:
1 Marcus Delgado 64% FHA 31 units · $11.8M
2 Hannah Brooks 52% FHA 27 units · $9.6M

Your account, your permissions

It sees exactly what you'd see

The connection signs in as you. Every question runs against your own account and the permissions you already have, so your assistant can reach what you could reach by logging in — and nothing else. Teammates connect with their own credentials, so access never travels with the setup.

Ask Model Match Share
How did my team's funded volume trend in May?
Team Volume · authenticated as you 1.8s

Your team · May 2026

Funded volume: $48.3M · up 6% from April

Units: 132 loans · 9 loan officers

Strongest market: Charlotte metro · 41% of volume

Scoped to your seat's permissions — the same data you'd see in the app, nothing from the open web.

Before you connect it

Something we didn't cover? Ask our team directly.

Give your AI something real to work with

Start a 14-day free trial, connect Model Match to Claude or ChatGPT in about two minutes, and stop checking your assistant's homework.