Complianceintelligenceformortgage case data.
We read the messy, unstructured inputs the industry writes off — scans of scans, call transcripts, handwritten debt-con sheets, email chains, CRM data dumps, Land Registry titles — and apply lender-specific policy to them at scale.
Built to the standards UK regulated lenders require
Designed for mortgage processing.
Built around what regulated lenders actually need — secure infrastructure, mortgage-specific document handling, and a real production track record.
Built for regulated environments
ISO 27001 certified. Hosted on Azure UK, architected around the controls FCA-regulated lenders are audited against — the same posture your infosec team already expects from core banking suppliers.
Mortgage-specific IP
Built from scratch for mortgage case files, not retrofitted from generic OCR. Handles scans of scans, encrypted PDFs, and mixed-quality packs as a first-class input, not an edge case.
Proven with lenders
Running in production today across UK regulated lenders and mortgage networks. Every deployment builds on the last, so yours is never the first.
Six ground truths
we built around.
Most mortgage-AI projects stall on the same six things. We designed Curvestone around them.
Mortgage case files are messy.
Scans of scans, encrypted files, handwritten notes, mixed-quality packs. Systems that assume clean inputs fail in production.
The FCA expects full traceability.
Every check traces back to a source document, an extracted value, and a policy rule. "The AI flagged it" isn't an audit trail.
Policy isn't a decision tree.
Beneath every policy doc is the layer underwriters apply from experience — exceptions, judgement calls, the "technically yes but actually no". Encoding just the page fails the real case.
Real configurability takes a proper setup.
Most vendors hand over a config interface and walk away. We stay hands-on with your SMEs until your team owns it.
Underwriters don't trust black boxes.
Underwriters are the quality gate. The platform must show its reasoning clearly enough that a senior reviewer reaches the same answer.
Generic platforms don't know what they don't know.
A configurable AI platform can be pointed at mortgage data — but it can't shortcut years of domain learning. You end up paying vendors to learn on the job.
Built like an underwriting team, not a single model.
Specialist-agent architecture
Every check runs as its own tuned agent, dispatched in parallel — not one model stretched across the whole case.
Document intelligence
Handles scans, photos, fact-finds, encrypted PDFs, and mixed-quality broker packs — every file cryptographically fingerprinted.
Underwriter-ready output
Case integrity, fraud, credit, lender criteria — returned as a PASS / REVIEW / FAIL report in 2–4 minutes.
Trust Agent (AI checking AI)
Daily benchmarking, drift detection, and regression suites against thousands of historical cases — backed by a contractual 95% accuracy SLA.
What changes for your lending operation.
Faster time-to-offer
Cases don't queue for manual document processing. Brokers see faster decisions. Competitive advantage.
Reduced cost per file
Automate the repetitive checking. Redeploy teams to complex cases and product innovation.
Consistent policy application
Same case, same rules, same result. Reduces internal disputes and rework.
Complete QA coverage
Check every case, not a sample. Evidence Consumer Duty and fair lending oversight for regulators.
Fewer audit surprises
Full, timestamped evidence trails for every decision and override.
Experts doing expert work
Senior underwriters and QA leads spend time on complex cases, not chasing documents.
Sits alongside your team and your stack — not in place of them.
Automating the compliance and judgement checks that today live in underwriters' heads and on scattered checklists.
Cases
LOS
Brokers
Underwriters
Compliance
Audit
Answers for lenders evaluating Curvestone.
See Curvestone analysea real loan file.
15-minute technical demo. We'll walk through your lending criteria and show you how Curvestone processes real applications — not sanitised examples.