Build the future of lending.
A loan originated today is indistinguishable from 30 years ago. We are building a category-defining platform for loan origination globally.
Lending is one of the largest unautomated cost centers in financial services
Originating a single mortgage in North America costs lenders roughly $9,000 and takes 40 to 60 days, almost all of it spent on document collection, verification, conditions management, and back-and-forth with borrowers. Multiply that across tens of millions of loans every year — mortgages, auto, commercial, SMB, consumer credit — and you get one of the largest opportunities for disruption in financial services.
Mortgages originated globally each year. Mortgages are one product category in one slice of lending.
Average cost to originate a single mortgage in North America.
Average mortgage time-to-close, most of it document and verification work.
Companies that own the origination infrastructure layer.
Payments got Stripe. Bank data got Plaid. Identity got Persona. Cloud got AWS. Lending has no equivalent. The reason is the bar — accuracy, auditability, deterministic controls, regulatory acceptance, and the trust of institutions that won't move decisions onto a vendor they don't fully believe in.
Whoever clears that bar first becomes the layer every lender, every broker, every underwriter, every capital allocator runs on. That's what we're building toward.
What makes this hard
This is not 'wrap a model around a PDF.' The bar is institutional trust.
Regulated
Every output has to defend itself to compliance, audit, and counsel. There's no 'move fast and break things' on a half-million-dollar mortgage.
Auditable
Decisions need a paper trail a regulator can read in six months. Every flag, classification, and number has to be traceable to its source.
Deterministic outputs
LLM models aren't deterministic. The numbers, conditions, and decisions need to be.
High-accuracy
Mortgage decisions are six- and seven-figure decisions per file. Confidently wrong is worse than slow.
Edge-case dense
Every income source, ownership structure, and document format is a long-tail problem. The work lives here.
Trust-gated
Institutions buy from companies they believe will still be here in five years. That trust is earned in real production capability, not demos.
The window is open now
AI has made it possible to understand messy borrower information at scale for the first time. Statements that took an analyst forty minutes can be parsed in seconds. Documents in twenty-two languages can be read like they're in one.
But lending can't be built on generic AI wrappers. Accuracy, auditability, consistency, and deterministic controls are non-negotiable in regulated financial workflows. The companies that cut those corners are not the companies institutions will trust with their capital.
The companies that win this category will combine AI capability with deep workflow understanding, rigorous verification, and infrastructure-grade reliability. That window is open now.
Why the right person should take this seriously
Calgary, in person
Founding teams move faster in the same room. We're hiring for people who agree.
AI-first internally
Not just in the product. Engineering, design, infra, support, sales — every function uses the best AI tooling we can get our hands on.
Real money, real files
Brokers and underwriters use the product all day on meaningful decisions. Everything you ship is consequential.
Founding team
Small enough that you'll work directly with every person in the company, and be expected to make an impact
Equity that matters
Total compensation is competitive and a blend of cash and meaningful equity. Joining now means joining when the upside is still being built.
Category-defining work
This is the company building the infrastructure layer for lending. There's no template for what we're doing.
Don't see your role below? We always want to hear from exceptional people. hello@purelend.ai.
Open Positions
Head of Client Success
Position FilledOwn the entire post-signature client journey — implementation, onboarding, ongoing success, and expansion. Build this function from scratch.
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