Month-end close is still one of the most manual and stressful processes in finance. Accounting teams spend too much time reconciling transactions, investigating exceptions, gathering support, and checking work under tight deadlines.

FinQloz is an AI-native financial close platform designed to automate much of that repetitive work while keeping finance professionals firmly in control. It helps teams reconcile accounts, identify and explain exceptions, prepare supporting evidence, and suggest next steps — so the close moves faster without sacrificing accuracy or control.

FinQloz is built by me, a 25-year accounting veteran who has lived this pain firsthand — through month-end closes, audits, reconciliations, reporting deadlines, and the constant pressure to get the numbers right. I’m building the product around how accounting teams actually work, based on problems I’ve experienced myself over many years.

What makes FinQloz different is trust. Every result is traceable to its source, important actions are supported by evidence, and nothing is posted without human review and approval. FinQloz combines AI with deterministic, repeatable processes so finance teams can understand not only the answer, but how the answer was reached.

The product is substantially built and is now ready for real-world validation. I’m looking for 2–5 design partners — either startups with an internal accounting or finance function, or outsourced accounting teams managing the close for their clients. The goal is to use FinQloz in real close workflows, gather candid feedback, and validate both the product and the market need.

I’m also beginning conversations with angel and pre-seed investors who believe there is an opportunity to rethink how accounting work gets done. Initial funding will help support product development, security, infrastructure, integrations, and onboarding the first customers.

The immediate goal is straightforward: put FinQloz into the hands of real accounting teams, prove that it can make the close faster, more predictable, and audit-ready, and learn directly from the first users before scaling further.