Karmen is a Construction Industry AI Scheduling Assistant that assists Teams with Complex Scheduling Tasks and Efficiencies. The company was founded in 2024 by Jonas Ebrahimi and Naman Wahi and is now a member of Y Combinator’s Portfolio. Construction Firms are the primary target for Karmen’s AI-driven solutions to Streamline Project Timelines and Resource Allocation.
The Company was Co-founded by Jonas Ebrahimi (Former Commodity Trader and Contractor Family Background) and Naman Wahi (AI Engineer for Shell, ExxonMobil, Suffolk).
Karmen was accepted into Y Combinator’s Winter 2024 Batch as a Construction AI Scheduling Assistant.
Karmen has raised $500,000 in Seed Funding to Develop their Construction AI Scheduling Platform.
Cloud-based SaaS platform
AI-powered scheduling optimization with machine learning for delay prediction and resource allocation, leveraging founders' construction domain expertise
Inferred from founders' backgrounds and product description; limited public technical documentation
AI-Driven VaR Estimation Based Upon Historical Payment Data and Market Trends for Invoice Financing Risk.
Measuring Tail-Risk for Non-Payment Scenarios in Factoring Receivables.
Real-Time Analysis of Customer Payment Behavior and Market Conditions.
Credit Risk Decomposition by Customer Portfolio and Invoice Characteristics.
Stress Testing for Payment Delays, Economic Downturns, and Customer Defaults.
Continuously Tracking Customer Credit Risk and Payment Patterns.
AI-Based Financing Recommendations based upon Invoice Risk Assessment.
Optimal Credit Line Allocation Against a Receivable's Portfolio.
Real-Time Invoice Risk Validation prior to Financing Approval.
Digital Financing Disbursement within 48 Hours.
Historical Performance Verification of Risk Models.
Payment Delay and Risk Threshold Notifications.
Attributing Risk to Customer Payment Behavior.
Optimizing Financing Terms based upon a Customer's Credit Profile
| Benchmark Criteria | Product Capability | Compliance Status | Notes |
|---|---|---|---|
| Standardization aligned with industry practices | Secure API integrations with accounting tools | COMPLIANT | Native connections to existing financial software |
| Full-pipeline coverage (data to decision) | End-to-end invoice financing workflow | COMPLIANT | Data analysis to financing disbursement |
| Continual learning for distribution shifts | Real-time customer behavior analysis | COMPLIANT | AI adapts to payment pattern changes |
| Robustness in low signal-to-noise environments | Historical payment data analysis | PARTIAL | Strong in B2B receivables domain |
| Relational financial data modeling | Customer relationship risk preservation | COMPLIANT | No invoice assignment required |
| Model explainability for compliance | Transparent AI risk assessment | COMPLIANT | Clear cost transparency and monitoring |
| Real-time backtesting with transaction costs | Digital transaction monitoring | COMPLIANT | Real-time receivables status tracking |
