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

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

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

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

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

• 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