There's a number that never shows up in the demo: the monthly bill. Plenty of enterprise AI looks brilliant in a pilot, and then a few months in, finance notices the running cost is higher than the human workflow it was supposed to replace. It's the quiet failure of enterprise AI, and the good news is it's fixable.
The token trap
Big, general-purpose models are priced by the token. Every question you ask and every answer you get back costs money, all day, every day.
Point one of those models at a high-volume workflow, say thousands of invoices, claims, or documents, and the bill scales with the volume of work, not with the value it creates. The busier the agent gets, the worse the maths.
ValueMaxxing, not TokenMaxxing
The fix isn't a bigger discount. It's a different goal. Most enterprise AI maximises tokens. We build agents that maximise value instead: the same intelligence, run privately, for a fraction of the cost.
It's a simple test to apply to any AI project: is it engineered to do the most work per dollar, or just to use the most capable (and most expensive) model available?
Small models, sized to the job
A model fine-tuned for one job, like reading an invoice, matching a voucher, or writing up a clinical note, needs far less computing power than a giant model that also has to be able to write poetry.
That's the difference between an agent that pays for itself and one that quietly runs up the bill. We size the hardware to the workload, not to the brochure.
Watch the bill from day one
Cost isn't a thing you check at the end of the quarter. We watch the running cost from the first day in production and manage it as volumes grow. Think of it as FinOps for AI, handled for you.
Done right, a focused agent runs roughly ten times cheaper in production than the general-purpose approach, and returns what it cost within months.