Most loan officers have used ChatGPT to write an email or brainstorm a social media post. That is a useful starting point -- but it barely scratches the surface of what AI can do for your mortgage business.
The next wave is agentic AI -- autonomous assistants that do not just respond to your prompts but proactively analyze your data, spot patterns you would miss, and tell you exactly what to do next. Think of it less like a chatbot and more like a brilliant analyst who has read every email in your inbox, reviewed every loan in your pipeline, and cross-referenced it all with market data -- then hands you a prioritized to-do list every morning.
Tools like ThirdFloor.ai, ProPair, and Lendware Predict are already doing this for mortgage teams across the country. Below, we break down how this technology works, what it looks like in practice, and how you can start using it to close more loans -- not by working harder, but by working on the right borrowers at the right time.
What Is Agentic AI -- and Why Should You Care?
ChatGPT / Basic AI
You give it a prompt, it gives you text. Helpful, but reactive and disconnected from your actual data.
- Writes emails when you ask
- No access to your CRM or LOS
- Cannot see your pipeline
- Generic advice, not personalized
Agentic AI
Proactively analyzes your data, detects signals, and takes action. It works while you sleep.
- Reviews your CRM, email, and LOS data
- Scores and ranks leads automatically
- Flags at-risk loans before they blow up
- Tells you exactly who to call and why
The key difference: ChatGPT helps you write faster. Agentic AI helps you decide faster -- who to call, when to call them, what to say, and which loans need your attention right now. It moves you from “AI for email writing” to AI that moves loans forward.
The Tools Leading the Charge
ThirdFloor.ai
The predictive platform built to run your mortgage business. Connects your entire tech stack and uses AI to surface daily insights on who to engage.
- Daily prioritized engagement list based on borrower signals
- Real-time conversion metrics and profitability tracking
- At-risk loan monitoring to prevent blown closings
- Intelligent automation for speed and scale
- Customizable playbooks per LO, branch, or lender
ProPair
AI-powered lead management that assigns scores to every prospect based on propensity to convert, then optimizes distribution across your team.
- Propensity-to-convert scoring for every lead
- Optimal lead-to-LO matching based on strengths
- Automatic re-prioritization as behavior changes
- CRM and LOS integration for real-time signals
- Performance analytics by source, LO, and product
Lendware Predict
Predictive analytics for mortgage portfolios. Uses AI to identify borrowers most likely to purchase, refinance, or churn -- before it happens.
- Purchase and refinance propensity modeling
- Churn prediction to retain at-risk borrowers
- Market timing signals based on rate movements
- Portfolio-wide opportunity scoring
- Integrates with Encompass, Byte, and other LOS platforms
From Data to Decisions: 5 Real-World Applications
Here is how agentic AI translates your existing data into actionable intelligence that directly impacts your close rate.
The Daily Hot List: Know Exactly Who to Call First
The Problem
You start each morning staring at a CRM full of leads with no clear sense of priority. You end up working them top-to-bottom or by whoever responded last, which means high-intent prospects get the same attention as tire-kickers.
How the AI Solves It
- 1The AI scans your CRM overnight -- every lead, every interaction, every data point.
- 2It cross-references borrower activity (email opens, website visits, rate-check requests) with behavioral patterns from thousands of past conversions.
- 3Each morning, it delivers a ranked list: your top 10-15 contacts for the day, with a brief explanation of WHY each one is high priority.
- 4As the day progresses and new signals come in (a borrower replies, a rate drops, a document is uploaded), the list updates in real time.
Real-World Scenario
You log in on a Tuesday morning. ThirdFloor.ai shows your daily engagement list. At the top: 'Maria Gonzalez -- opened your rate comparison email 3 times in the last 24 hours, visited your pre-approval page twice, credit pull expires in 5 days.' Below that: 'James Chen -- realtor partner asked about his file yesterday, appraisal came back clean, needs closing disclosure review.' You know exactly where to start.
Result: Instead of spending 30 minutes deciding who to call, you are on the phone with Maria by 8:15am. She was actively comparing lenders -- your perfectly timed call wins the deal. You would have gotten to her eventually without the AI, but by then she would have already locked with a competitor.
At-Risk Loan Alerts: Save Deals Before They Fall Apart
The Problem
Loans blow up in processing and underwriting all the time -- missing documents, expired rate locks, appraisal issues, borrowers going silent. By the time you find out, it is often too late to recover.
How the AI Solves It
- 1The AI continuously monitors every active loan in your pipeline against key risk indicators.
- 2It detects patterns that precede fallout: a borrower who stops responding to emails, a condition that has been outstanding for 5+ days, a rate lock expiring in 72 hours with no CTC.
- 3When risk is detected, you get an alert with the specific issue and a recommended action.
- 4Historical data shows you the probability of fallout if the issue is not addressed within a specific timeframe.
Real-World Scenario
It is Thursday afternoon. ProPair flags the Williams file: 'Risk Level: HIGH. Borrower has not responded to the last 3 emails over 8 days. Outstanding conditions: bank statement (requested 6 days ago). Rate lock expires Monday. Recommended: Call borrower directly and text backup contact.' You call immediately, reach Mrs. Williams, and learn she was confused about which bank statement was needed. You walk her through it on the phone. She uploads it that evening.
Result: Without the alert, you would have discovered the problem Monday morning -- after the rate lock expired. Extending the lock would have cost the borrower an extra $1,200, possibly killing the deal. The AI saved the loan and the relationship.
Refinance and Repeat-Buyer Triggers: Mine Your Past Database
The Problem
You have hundreds (maybe thousands) of past clients in your database. You know some of them could benefit from a refinance or are ready to buy again, but manually reviewing every file against current rates and timelines is impossible.
How the AI Solves It
- 1The AI maps your entire past-client database against current market conditions -- rates, home values, loan-to-value ratios, and time since origination.
- 2It identifies borrowers who would materially benefit from a refinance (e.g., rate is 0.75%+ higher than current market, PMI can be removed, cash-out equity is available).
- 3For past buyers, it tracks life-event signals: home anniversary dates, average move-up timelines for the area, and any engagement with your content.
- 4Each week, you get a list of past clients to re-engage with a specific reason to reach out.
Real-World Scenario
Lendware Predict scans your 400-person past-client database on a Friday. It surfaces 12 borrowers: 'These 12 clients currently have rates between 7.25% and 7.75%. Current 30-year rates are 6.15%. Estimated monthly savings range from $185 to $340. Combined refinance volume: approximately $3.8M.' It also flags 3 past buyers who purchased starter homes 4-5 years ago and are in zip codes where the average move-up timeline is 4.5 years.
Result: You spend Monday calling the 12 refinance candidates with specific numbers: 'Hi Tom, rates have dropped to 6.15% and based on your current loan I can save you about $240 a month -- that is nearly $3,000 a year.' Four of the twelve convert. That is $1.6M in new volume from your existing database, with zero marketing spend.
Realtor Partner Intelligence: Strengthen Your Best Relationships
The Problem
You work with 15 realtors but only 3 send you consistent business. You do not have a clear picture of which relationships are growing, which are stagnating, and which need attention -- until a referral stops coming and you realize too late.
How the AI Solves It
- 1The AI tracks every referral, every closed deal, and every interaction with each realtor partner.
- 2It calculates referral velocity (deals per month), conversion rate (referrals that close), and trend direction (increasing, flat, or declining).
- 3When a previously active partner goes quiet -- no referrals in 30+ days when their average is 2 per month -- the AI flags it.
- 4It also identifies realtors you have worked with once who have high potential based on their transaction volume.
Real-World Scenario
ThirdFloor.ai surfaces a partner insight: 'Alert: Jennifer Martinez -- referrals down 60% over the last 45 days (from 3/month to 1). Last interaction was 22 days ago. She closed 28 buy-side transactions last year, making her your highest-volume partner. Recommended: Schedule a coffee meeting or send a market update she can share with her buyers.' Separately, it flags a new opportunity: 'Agent David Park sent you one referral 3 months ago that closed. He did 19 transactions last year but currently splits business across 3 LOs.'
Result: You take Jennifer to lunch that week and learn she felt out of the loop on her last two clients' loan statuses. You set up automated milestone updates (see our Cowork and Copilot article) and the relationship recovers. You also start sending David a weekly rate sheet and close two more deals with him over the next quarter.
Pipeline Forecasting: See the Future, Not Just the Present
The Problem
Your pipeline report shows you what is in progress, but it does not tell you what is likely to close, when it will close, or where the gaps are. You are always reacting instead of planning.
How the AI Solves It
- 1The AI analyzes every loan in your pipeline and assigns a probability-to-close score based on dozens of factors: borrower responsiveness, document completion rate, time in current stage, historical patterns for similar loan profiles, and market conditions.
- 2It generates a weekly forecast: expected closings, expected revenue, and pipeline gaps where you need more leads to hit your monthly target.
- 3It also models scenarios: 'If rates drop 0.25% next week, here are 8 fence-sitters likely to move forward.'
- 4Over time, it learns your specific patterns and becomes increasingly accurate.
Real-World Scenario
It is the first Monday of the month. ProPair shows your pipeline forecast: '23 active loans. Predicted closings this month: 14 (probability-weighted volume: $4.2M). Expected commission: $42,000. Gap to monthly target: 3 loans / $900K. Recommended: Focus on the 4 pre-approval clients with >70% close probability who have been active in the last 7 days. Also: 2 loans at risk of slipping to next month -- the Patel file (waiting on appraisal, 6 days overdue) and the Robinson file (borrower unresponsive since Thursday).'
Result: You know exactly where you stand before your first cup of coffee. Instead of a vague sense that things are 'busy,' you have a clear picture: you need 3 more loans, and the AI has told you exactly which prospects to focus on and which active deals need intervention. Your branch manager asks for your forecast at the team meeting -- you are the only one with a real answer.
The Numbers: Why Prioritization Matters
21x
More likely to convert when contacted within 5 min
60%
Of leads lost to poor follow-up timing
5-12
Touchpoints needed before a borrower applies
30-45
Average days from first inquiry to application
The bottom line: In a business where timing is everything, guessing who to call next is the most expensive mistake you can make. AI prioritization does not replace your expertise -- it makes sure your expertise is pointed at the right person at the right moment. The LOs who adopt this technology are not just closing more loans; they are closing the same number of loans in half the time, freeing up capacity to grow without burning out.
Getting Started: A Practical Roadmap
Week 1
Audit Your Data
- Inventory your current tech stack: CRM, LOS, email, phone system
- Identify where your borrower data lives and how connected (or siloed) it is
- Clean up your CRM: remove duplicates, update statuses, tag past clients by loan type and date
- Calculate your current conversion rate by lead source -- this is your baseline
Week 2
Evaluate and Connect
- Request demos from ThirdFloor.ai, ProPair, and/or Lendware Predict
- Ask specific questions: 'Can you connect to my CRM? My LOS? How long to onboard?'
- Check with your compliance team -- most of these tools are data-analysis platforms, not borrower-facing, but verify
- Choose the tool that best fits your tech stack and team size
Week 3-4
Pilot and Measure
- Start with ONE use case: the daily hot list is the easiest win
- Follow the AI's recommendations for 30 days -- call the people it says to call, in the order it suggests
- Track your results: conversion rate, speed-to-contact, deals closed vs. the prior month
- Expand to at-risk alerts and refinance triggers once the daily list is habit
Month 2+
Scale and Optimize
- Add pipeline forecasting and realtor partner intelligence
- Share insights with your team or branch manager to improve group performance
- Use the AI's feedback to refine your processes -- it learns from your outcomes
- Combine with MLO Assistant prompts and automation tools for a fully connected system
Pro Tips from Top Producers
Trust the data, not your gut -- at first
The hardest part of adopting AI prioritization is overriding your instincts. You will look at the hot list and think, 'There is no way that lead is ready.' Call them anyway. After 30 days, compare the AI's picks to your gut picks. The data almost always wins.
Feed the machine better data
AI is only as good as the data it can see. Log your calls, update lead statuses promptly, and connect as many data sources as possible. The LOs who get the best results are the ones with the cleanest CRM hygiene.
Use AI insights to train, not just to sell
If you manage a team, the prioritization data is a goldmine for coaching. You can see which LOs are converting high-probability leads and which are not -- then figure out why. It turns subjective performance reviews into objective, data-driven coaching sessions.
Combine insights with automation
Knowing who to call is step one. Automating what happens before and after the call is step two. Pair your AI insights tool with MLO Assistant prompts for email copy, and a CRM copilot like Cowork for automated follow-up sequences. The stack multiplies itself.
Start with past clients
Your past-client database is the lowest-hanging fruit for AI prioritization. These are people who already trust you. When the AI identifies a refinance opportunity or a likely move-up buyer, that call converts at 3-5x the rate of a cold lead. Start there.
The Bottom Line
The mortgage industry is moving from “AI as a writing tool” to “AI as a decision engine.” Tools like ThirdFloor.ai, ProPair, and Lendware Predict represent the next evolution -- they do not just help you work faster, they help you work smarter by putting the right borrower in front of you at the right time.
The loan officers who adopt this technology early will not just have a competitive advantage -- they will have a structural advantage. While competitors are still scrolling through their CRM trying to figure out who to call, you will already be on the phone with a borrower who is ready to move forward. That is how you close more loans without working more hours.
Start with the daily hot list. Measure the results. Then expand. The AI does not replace you -- it makes you the most dangerous version of yourself.