Launching today
Copperlane
Turn hours of loan processing into seconds
513 followers
Turn hours of loan processing into seconds
513 followers
Copperlane is an AI-native loan origination system. Our AI agent, Penny, optimizes rate pricing, guides borrowers, and verifies documents - cutting loan processing time from hours to seconds.
Copperlane
Hey PH! I’m Brianna, co-founder of Copperlane (YC W26)!
The Problem
Lenders spend $11,800 to originate a mortgage, and the majority of that cost is burned during intake due to missing documents, back-and-forth, and costly errors. This manual process breaks down on almost every loan, across 8 million loans a year.
Existing tools fall into two approaches:
❌ Legacy portals – These systems are decades old, and loan officers need to chase down borrowers for docs manually.
❌ AI built by older mortgage teams – Our competitors are all mortgage people trying to build AI. We’re the only AI people learning about and building for mortgage. :)
My co-founder and I both come from mortgage families (Freddie, Fannie, FHA), so the space is personal to us and we built Copperlane to fix it.
How Copperlane is Different 🚀
Copperlane is an AI-native loan origination system powered by our agent, Penny, who behaves like a real loan officer.
🔸 Penny handles intake proactively – She pulls borrower docs, reads them, and checks the borrower’s loan eligibility.
🔸 Guides borrowers through the application – Penny answers questions and proactively helps borrowers complete the loan application correctly.
🔸 Proactively fixes issues – Is the borrower missing a paystub? Or reported conflicting income? Penny flags it immediately and follows up with the borrower.
🔸 Delivers clean files to lenders – Loan officers receive clear, organized files and can focus on bringing in new loans.
Who this is for
If you’re a lender or loan officer dealing with slow intake, Penny helps you close loans faster without extra headcount.
📎 Get started today
We’d love to show you what Penny can do! You can book a demo at here.
Carpe Diem,
– Brianna & Athan
@brianna_linHi Brianna, congrats on launching Copperlane, Penny sounds like a really smart way to tackle the messy mortgage intake process.
I’m currently researching how founders track and improve the operational health of complex SaaS products, especially ones powered by AI agents. I’d love to hear your perspective.
Would you be open to a quick 3–5 minute chat or sharing a few insights? It would really help my research.
@brianna_lin Hi Briana, indeed this was a long term waiting on filling the gaps from the underwriting side to handle complex paperwork. I am a business advisor and work with lots of lenders in SBA 7a and conventional loans. Is your model strictly for residential mortgages or can it be utilized for business or commercial properties?
Copperlane
@amir_dawdani Hey! We're primarily focused on mortgages/residential at the moment, but are open to exploring commercial loans in the future.
The document verification piece is what catches my attention — that's historically been the biggest bottleneck in loan processing, not the rate calculation. Curious how Penny handles edge cases like inconsistent income documentation for self-employed borrowers, that's usually where automated systems fall apart and a human has to step back in. If you've solved that reliably this could be genuinely transformative for smaller lenders who can't afford large underwriting teams.
Copperlane
@zerodarkhub Hey! Great question, so Penny flags inconsistencies across documents (like mismatched income signals) and surfaces them with clear explanations so loan officers can quickly review edge cases.
Copperlane
@vouchy I think seeing how the 2008 crisis affected so many families, and later watching loan files stall over missing docs, made it clear how broken the process still is. That's what drove us :)
Copperlane
@alamenigma Thanks so much! In those cases Penny flags inconsistencies and surfaces them clearly for the loan officer. Penny will also proactively chase after borrowers over SMS/email/phone.
How does Penny handle complex or non-standard borrower scenarios, such as self-employment income or inconsistent documentation, while ensuring full compliance with mortgage lending regulations?
Copperlane
@mordrag Hey Denis! So Penny flags things like self-employment income or inconsistent docs early, asks the borrower for the right supporting documents, and organizes everything for the loan team so underwriting gets a clean/compliant file.
What usually kills these flows is the resend-this-doc loop. Copperlane looks strongest where it flags edge cases before underwriting, because W-2 auto-fill matters less than giving ops one clean place to clear missing docs and big deposits.
Copperlane
@piroune_balachandran Hey Piroune! Exactly, so the resend-this-doc loop is what slows everything down. Penny focuses on catching missing docs, big deposits, and inconsistencies early and giving ops one clean place to resolve them before underwriting.
The "AI people learning mortgage" vs "mortgage people trying to build AI" framing is sharp and honest. Having Penny proactively flag issues like missing paystubs instead of waiting for a loan officer to catch it manually is where the real time savings happen. Congrats on YC W26, Brianna and Athan.
Copperlane
@siddhant_khurana Appreciate that! That’s exactly the idea behind Penny -- catching issues like missing paystubs early and keeping the file clean so loan officers don’t have to hunt through documents later.