Mortgage AI Quote Generator in Indiana

The intake form asks six to eight questions tuned for mortgage: type of inquiry (purchase, refinance, cash-out refinance, HELOC, pre-approval consultation), property type (single-family, condo, multi-unit, manufactured), purchase price or current home value, down payment or equity percentage, credit score range (excellent 740+, good 680-739, fair 620-679, building), employment type (W-2, self-employed, retired, mixed), state, and an email and phone. The form submits into n8n.

The workflow normalizes the inputs, runs them through an OpenAI prompt seeded with current rate sheet logic (the broker updates the rate floor and ceiling weekly), product matching (which loan products the borrower qualifies for based on credit, down payment, and employment), monthly principal-and-interest payment calculation, PMI calculation when down payment is under twenty percent, and closing cost estimation at the broker's typical state averages. The JSON estimate gets templated into a branded HTML email with the broker's logo and NMLS disclosure, a rate range across the products the borrower qualifies for, a monthly payment estimate, a closing cost estimate, and a one-click booking link for the application call.

A matching SMS fires through Twilio. Total time from form submit to estimate in hand, around thirty-five seconds.

Mortgage borrowers have a forty-eight-hour decision window. Once they have decided to refinance or to start pre-approval shopping, they will lock the rate with whichever broker quotes credibly first, because rates move daily and waiting feels expensive. The broker who responds with a real rate inside the first hour wins the relationship for the entire loan and often for the next refinance or purchase too.

Most brokers fail at the speed game because they are in an application call with another borrower, the inquiry from this morning gets a callback at 5pm, and the borrower has already locked with Rocket or a competitor. The broker sees the inquiry in their CRM, sees no engagement, and assumes the borrower was rate-shopping casually. They were, and they locked with the broker who responded fastest.

Speed wins the application, the application wins the closing, the closing wins the future refinance and the home purchase referrals.

A median purchase mortgage at three hundred fifty thousand with a typical broker compensation of one to one-point-five percent of the loan amount pays the broker thirty-five to fifty-three hundred per closed loan. A jumbo or cash-out refi pays more.

A mortgage broker pulling forty inbound inquiries a month and closing four at ten percent close rate is roughly industry average. Push close rate to twenty-five percent on instant-quote leads, which is realistic because the borrower is anchored to the broker who quoted first with a credible rate and payment, and the broker adds six extra closed loans a month at a blended comp of four thousand.

That is twenty-four thousand a month in extra commission on lead flow they are already paying for. The retainer pays for itself in the first week, and the lifetime value of a borrower (future refi, home purchase, referrals to family) compounds for years.

Complete n8n workflow with the Tally trigger, field normalization, OpenAI quote generation, email templating with NMLS disclosure compliance, and Twilio SMS dispatch. Tally form schema with mortgage-specific questions tuned to the minimum data the broker needs to give a credible rate range without triggering a TRID disclosure obligation. OpenAI system prompt seeded with rate-sheet logic that the broker can update weekly with their current rate floor and ceiling across conventional thirty-year, fifteen-year, FHA, VA, USDA, jumbo, and ARM products, with PMI calculation logic, closing cost estimation by state, and refinance break-even analysis.

Branded HTML email template with the broker's logo, headshot, NMLS number, the rate-and-payment estimate, and a clear next-step CTA for the application call. Twilio SMS template that fires alongside the email with the headline rate. Setup guide for the OpenAI key, the Twilio number, the domain authentication, the NMLS compliance language, and the brand swap.

Also included: a three-touch follow-up sequence for unbooked applications.

Indiana has 7 million residents distributed across major metros including Indianapolis, Fort Wayne, Evansville, South Bend, and Carmel. Indianapolis metro is the dominant market. Indiana's plumbing-specific state board contrasts with electrical and HVAC being local. Storm-driven roofing market active in central Indiana.

The seasonality of mortgage work in Indiana is the single biggest factor that shapes how this ai quote generator actually performs in the market. Four-season cycle. Winter heating dominant; summer cooling significant. The template's qualification logic, dispatch rules, and conversation flow are tuned to handle these patterns rather than forcing the agency operator to customize from scratch. Shops that deploy this in Indiana markets see the seasonality framing show up in the conversations from the first call.

Regulatory framework for mortgage brokers in Indiana varies at the local level rather than statewide, which is worth understanding because licensing references in customer conversations need to match local jurisdiction. The agent template handles this correctly by deferring licensing-specific questions to local context rather than asserting state-level rules that may not apply.

Plan three hours including the screen-share with the broker. You import the n8n workflow, paste the Tally form into the broker's website, wire in their domain so the email comes from the brokerage name, swap in the logo, headshot, NMLS number, and brand colors, and test by submitting a fake quote for a three hundred fifty thousand purchase with twenty percent down on a single-family home with a 740 credit score. The rate logic in the OpenAI prompt benefits from a real call with the broker: they will want to set the current rate floor and ceiling across the products they originate, set the closing cost percentages for the states they operate in, tune the PMI calculation based on the lenders they use, and confirm the NMLS compliance language in the email.

That conversation takes thirty to forty-five minutes. The broker should update the rate sheet weekly, which is a five-minute task in the n8n config. Once tuned, the system runs without intervention.

Agency operators bill setup at five hundred to a thousand, retainer at four hundred to seven hundred a month.