The problem Invoice Now solves

Invoice financing represents a $3+ trillion global market built on a fundamental structural inefficiency: suppliers who have completed legitimate work and hold confirmed invoices are forced to wait 30, 60, or even 90 days for payment while their capital sits frozen in receivables. This liquidity gap creates cascading operational risk; suppliers cannot pay their own vendors, fund new production, or invest in growth while waiting on buyers to fulfill payment terms. The existing solutions to this problem are fragmented and opaque: traditional factoring companies offer take-it-or-leave-it rates with no competitive transparency, and suppliers have no mechanism to verify whether the terms they receive reflect fair market pricing. Invoice Now addresses this directly by creating a competitive auction marketplace where invoicees can sell confirmed invoices immediately, financiers bid in real time for the right to fund those invoices, and the resulting market dynamics produce rates anchored to genuine supply and demand rather than a single intermediary's margin requirements.

Challenges we ran into

One of the biggest hurdles our team faced was verifying the legitimacy of financiers and invoicees without compromising user privacy. Because our platform handles financial transactions, trust is essential, but traditional verification methods like KYC require collecting and storing sensitive data, which conflicted with our goal of building a privacy-preserving system. After researching alternative approaches, we determined that Zero-Knowledge (ZK) proofs offered the best solution, allowing users to prove they meet verification requirements without revealing their underlying information. Although integrating ZK proofs required us to rethink our validation logic and overcome a technical learning curve, it ultimately enabled us to strengthen platform security, preserve user privacy, and enhance overall trust in the system.

Use of AI tools and agents

In Invoice Now, we use AI to streamline the invoice submission process and reduce friction for users. Specifically, we implemented an AI-powered invoice parser that automatically extracts structured data from uploaded documents. Instead of requiring users to enter every field manually, the system processes PDFs or images using document-understanding models that identify key text regions, classify fields, and normalize the data into a standardized format for our platform.