Bilrost
Automated infrastructure for modern commercial lenders.
About
Every credit decision runs on context, OMs, tax, financials, borrower history, the judgment of your best underwriters, scattered across systems that don't talk to each other. The hard part of automating credit was never the doc processing. It was turning that context into something accurate enough to act on and auditable enough to defend. Bilrost is AI-native infrastructure for commercial credit. We turn the documents behind every deal into a structured context graph that powers the full lifecycle — intake, underwriting, servicing, monitoring — collapsing weeks of work down to minutes, with an audit trail risk and compliance can stand behind. We've already processed over 10,000 deals and are piloting with top lenders. Because the intelligence lives in the context graph, not a black-box prompt, every output is transparent, traceable, and compounds across lenders into intelligence no single institution could build alone.
From their website
bilrost.ai ↗Bilrost provides AI-powered automation for commercial loan processing, enabling faster, decision-ready underwriting outputs by ingesting documents, validating data, and standardizing inputs within a lender's existing systems.
Bilrost automates commercial loan processing end-to-end: it ingests deal files from email, portals, and cloud storage; validates and flags discrepancies (with field-level traceability); standardizes financial data and reconciles information; and underwrites by populating lender-specific underwriting models or Excel/LOS inputs, producing decision-ready outputs. It integrates with LOS, cloud storage, email, and data systems, maintains structured memory across deals, and offers modules that can be adopted end-to-end or used incrementally.
Who it’s for: Commercial lenders and financial institutions processing CRE and business-purpose loans
- Automated document ingestion from multiple channels
- Field-level discrepancy detection and source traceability
- Data standardization and normalization for underwriting
- Population of lender-specific underwriting models
- Structured inputs for underwriting and decision-ready outputs
- Portfolio risk monitoring and covenant tracking
- Seamless integration with LOS and data systems
Hiring/traction mentioned; enterprise-focused features; references to thousands of deals and enterprise-grade security
Founders · 2
Silvia grew up around large-scale real estate development, seeing firsthand how underwriting decisions determine whether projects move forward, and how much of the process remains manual. She now leads Bilrost, building autonomous loan infrastructure to modernize real estate finance. She previously led product at Blockchain.com, where her team drove 30x trading volume growth and secured over $1B in AUM. Earlier, she worked with AI pioneer David Ferrucci at Bridgewater and conducted deep learning research at MIT.
I’m a founder-CTO focused on building AI-native systems that turn messy, real-world information into reliable, scalable products. At Bilrost, I’m building infrastructure that applies modern AI and systems engineering to high-stakes financial workflows—where accuracy, traceability, and performance actually matter. My work sits at the intersection of AI-driven automation, developer productivity, and large-scale platform architecture, with a strong bias toward shipping real products, not demos. Before Bilrost, I spent two decades building and scaling consumer and enterprise platforms used by millions—from mobile-first products to low-latency, high-throughput backend systems operating at global scale. I’ve led organizations, shipped dozens of production apps, modernized legacy platforms, and driven architectural transitions that materially improved cost, reliability, and speed.
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