Join beta — limited spots open for finance teams

Why us?

We built ALFRD because the first weeks of finance work should not be data archaeology.

We kept watching the same thing happen: experienced finance professionals spending their first weeks on a new engagement just figuring out what's wrong with the data. Not advising. Not analysing. Just cleaning.

Financial decisions are only as good as the data behind them. Most of that data has not been checked. We're fixing that.

Meet our team

Sammi Teki, Founder & CEO

Sammi Teki

Founder & CEO

Bachelor of Applied Science, Business

Queen Mary University of London, UK

Built AI infrastructure and automation systems across 15+ startup and mid-market engagements through Badr Studios. Discovered how much time finance teams waste cleaning data before they can start advising through dozens of discovery conversations with fractional CFOs, and built ALFRD to fix it.

Igwe Francis, Co-founder

Igwe Francis

Co-founder

Bachelor of Science, Software Development

BYU-Idaho, US (via BYU-Pathway Worldwide)

3+ years experience in fullstack development and automation. Taking disorganised startup data and reshaping it into clean systems built for AI agents and automated workflows. Leading the product build across the ALFRD Checks workflow, from upload flow to findings report.

Harmin Patel, Founding Software Engineer

Harmin Patel

Founding Software Engineer

Bachelor of Applied Computer Science

Dalhousie University, Canada

1 year of R&D software development experience focused on AI infrastructure. Building and maintaining ALFRD's data processing pipelines and contributing to the Health Check tool engineering: the system that ingests, validates, and reports on financial data quality at scale.

Peter Hubshman

Peter Hubshman

CFO Design Partner

MPPM, Finance, Management, Org. Behavior

Yale School of Management, US

18+ years as a fractional CFO across 15 companies, from owner-managed SMEs to Series C startups and PE-backed roll-ups.

Built the ALFRD Checks logic from real engagements where someone had to find the issues manually.