Most AI startups track revenue carefully. Almost none track what their AI actually costs per output. I help you fix that — before it becomes a crisis.
In 2024, most startups had one major variable cost: headcount. You could see it on your P&L. You could model it. You could control it.
In 2026, AI-native companies have a new variable cost that most finance teams cannot see, cannot model, and cannot control — inference spend. Every API call, every agent loop, every LLM completion is a cost accumulating silently.
I have seen companies spending $15,000 a month on inference with zero visibility into whether a single dollar was generating revenue. No cost-per-output metric. No gross margin per AI feature. No financial circuit breaker on runaway agents.
That is the gap InferenceCFO was built to close.
I started as a bookkeeper. I know what clean books look like, how to reconcile accounts under pressure, and how to translate financial data into decisions a founder can act on.
Over 7 years working with businesses across the UK, Saudi Arabia, and globally, I noticed a pattern: the smarter companies were getting, the less visible their costs became. AI changed everything — and most finance teams were not keeping up.
I built InferenceCFO.com to be the financial advisory practice that AI-native companies actually need — not a traditional CFO who learned what an API is last year, but a financial operator who understands token billing, agent architectures, and usage-based cost curves from the ground up.
Start with a free 15-minute Efficiency Audit. No commitment, no pitch. Just an honest look at your AI spend and one clear recommendation.