AI in mortgages that actually saves time, not hype.

On this episode of Mortgage Tech Talks I sit down with Lucas Scheer, co-founder of Purelend. We dig into what AI can do today for brokers, why trust still wins in real estate and lending, and how document collection, usable income, and down payment verification can be automated without losing the human touch.

What you’ll learn

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  The real advantage for brokers who use AI vs those stuck in manual review

  
  


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  How Purelend structures down payment checks, stated income analysis, and doc organisation for lenders and brokers

  
  


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  Where full automation fails and why a copilot model makes more sense

  
  


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  Accuracy, hallucinations, and how to combine human review with AI to raise overall quality

  
  


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  Practical privacy and workflow considerations when rolling AI into your shop

  
  

Chapters

00:00 AI will not replace you. People using AI will

00:26 Set up for the episode

00:40 Intro and guest welcome

01:01 What Purelend does for brokers and lenders

02:24 Lucas’ background, NEO Financial, and the path to Purelend

03:10 The 60,000 km motorcycle detour and lessons

04:10 Building applied AI before ChatGPT

05:13 Pre-2020 AI adoption vs the post-ChatGPT shift

06:19 Focusing on outcomes over buzzwords

07:33 Media hype, fear, and what actually matters to a brokerage

08:28 Useful analogies for explaining AI to clients

09:03 Common fears brokers raise

10:11 Two brokers, two results. Why leverage wins

11:26 Excel analogy and why AI is the new table stakes

12:07 Privacy, data, and realistic risk

12:46 Will AI do prospecting and client calls

13:39 Why trust and emotion keep humans in the loop

15:14 Where automation helps and where it breaks

16:20 Purelend elevator pitch

16:46 Duplicate effort across brokers and lenders

17:46 Speed, cost basis, and winning deals

18:10 Why this only became feasible recently

19:18 Feature set: down payment, usable income, organising docs

20:02 Stated income launch and common gotchas

21:06 Borrower alerts without adding friction

21:39 Full auto vs copilot. Drawing the line

24:12 Specific knowledge that AI cannot replace

25:38 Borrower portal vision and UX tradeoffs

30:44 Accuracy, hallucinations, and safeguards

32:27 Probabilistic vs deterministic and hitting 92 to 96 percent

35:04 Benchmarking against human accuracy

38:50 Supervisors, different error patterns, and combined accuracy

40:38 Where to learn more

40:55 Close