Asset Lenz
Asset Lenz provides predictive analytics for private-market investors, layering a live quant engine onto existing Excel workbooks to enable dynamic scenario analysis and risk modeling. The platform uses AI to extract data from deal documents and prices every holding—private and public—on a single set of simulated macro paths, unifying deal-level rigor with portfolio-level intelligence.
- Data & Analytics
- Financial Technology
- Artificial Intelligence
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Private-market investors such as pension plans, sovereign wealth funds, and family offices struggle to perform rigorous quantitative analysis on illiquid assets, which are typically modeled in static Excel spreadsheets with unstructured assumptions. Traditional approaches rely on appraised, smoothed, and lagged valuations that obscure true risk and make it difficult to compare private deals against public holdings or a reference portfolio.
Solution
Asset Lenz provides a predictive analytics platform that layers a live quant engine onto existing Excel workbooks, preserving firm-specific logic while adding structured assumptions, audit trails, and AI-driven data extraction. The platform generates a single set of simulated macro paths used to price every holding—private and public—so returns align path by path and risk, return, and liquidity read consistently from a single deal to the total fund. AI handles document extraction and financial spreading from virtual data rooms, producing a validated first-draft model in minutes. The platform supports the Total Portfolio Approach by measuring each deal's exposure and active risk against a passive reference portfolio priced on the same simulated paths.
Target Audience
Primary customers are institutional private-market investors including pension plans, sovereign wealth funds, family offices, and general partners, specifically deal teams, risk managers, and CIOs/portfolio managers.
Features
- Live quant layer on existing Excel workbooks that preserves firm-specific logic while making static data dynamic, traceable, and ready for scenario analysis
- AI-powered document extraction and financial spreading from virtual data room uploads, generating a validated first-draft model in minutes
- Single set of simulated macro paths used to value every holding, public and private, enabling apples-to-apples comparisons across asset classes
- Factor-level risk attribution (growth, interest-rate, currency, inflation) at holding, book, and fund level, with VaR and CVaR calculations linked to fundamental deal drivers
- Total Portfolio Approach implementation with a passive reference portfolio priced on the same simulated paths, measuring each deal's active risk against it
- MCP-compatible agentic platform integration, allowing enterprise AI assistants to compute figures through the platform rather than improvising
- SOC 2 Type II (in progress) and GDPR compliance with tenant-isolated database architecture and AES-256 encryption