Autonomous valuation systems for the AI era. Qiymat ingests pitch decks and financials to generate audit-ready valuation models using 11 specialized AI agents.
Valuing private companies is slow, manual, and prone to human bias. Analysts spend weeks parsing PDFs and spreadsheets, often missing critical data flags or market benchmarks. Traditional methods lack the scale for rapid deal-making.
Analysts miss critical risks due to emotional bias.
Weeks extra for PDF/Excel parsing at fund level.
"Qiymat has transformed how investment funds analyze private data, enabling 10x higher deal velocity with unmatched accuracy. The multi-agent debate system ensures every valuation is defensible and free from human error."
Qiymat is built on a high-velocity Model Context Protocol (MCP) backbone, connecting autonomous agents to 87+ specialized tools. Unlike static systems, Qiymat agents gather fresh market data, live OSINT, and real-time financial telemetry to ensure every valuation is defensible and current.
Autonomous ingestion and normalization of complex PDF and Excel data.
Simulated war-room where LLMs challenge each other to eliminate bias.
Live stochastic modeling using real-time market telemetry.
Audit-ready reports generated with deep structural integrity.
Evaluates the fidelity and readiness of raw financial data for high-stakes AI analysis.
Connects models to regional fiscal benchmarks and geographic risk telemetry.
Real-time open source intelligence for identity and market claim verification.
Direct indexing and parsing of massive PDF, Excel, and legacy file archives.
Real-time auditing of historical transaction databases and deal-flow history.
Automated ESG auditing based on live corporate reporting and supply chain data.
Financial stress-testing using ultra-fast stochastic modeling and Monte Carlo tools.
Indexes millions of academic and industrial papers for sector-specific benchmarking.
Direct connection to private equity and public market price aggregators.
Enterprise-grade extraction of balance sheets and cap tables from complex PDFs.
Predicts long-term revenue stability using customer sentiment and retention data.
Cross-checks documents against 100+ global identity and legal registries.