This analysis underpins critical financial decisions, including mergers, debt restructurings, and revenue recognition, where errors can lead to restatements, regulatory inquiries, and material financial impact. While many accountants have begun using general-purpose AI tools to manage increasing workloads, such tools often lack auditability. They may generate unverifiable or incorrect citations and fail to provide a clear connection between conclusions and underlying accounting standards.

Quillon addresses this gap through a platform built on a proprietary knowledge graph of accounting standards, integrated with EDGAR. It enables users to navigate accounting questions step by step, linking each conclusion directly to source material, while combining research, contract analysis, peer benchmarking, and memo drafting in a single workflow. The system allows accountants to review, edit, and validate each stage, maintaining human oversight while increasing efficiency, with every output traceable to its originating source.

"For two years we've watched technical accountants try to use general-purpose AI to keep up with a workload that's outpaced hiring," said Nikolay Dakov, co-founder and CEO of Quillon. They couldn't - because no auditor will defend output that the AI cannot cite. We built Quillon to close that gap: a workspace where the AI does the analysis, the accountant drives at every step, and every claim traces back to the exact paragraph in the standards.

The platform initially focuses on technical accounting memos, which inform nearly all financial reporting decisions within public companies. The company plans to expand into broader financial reporting workflows, including quarterly and annual disclosures. The funding will be used to expand Quillon’s engineering and go-to-market capabilities, supporting its transition from a research-oriented product to a platform that produces finalised deliverables and performs end-to-end accounting workflows.