Every line of AI spend, integrated, tagged, and reconciled into reporting your board can read, your auditors can defend, and you can confidently forecast off of.
★ R&D credit eligible
Last year, the mandate was simple: spend more on AI, ship faster, ask questions later. This year the questions arrived. AI is now approaching 2% of revenue and doubling annually. It's scattered across API bills, usage-based product costs, seat licenses, and a credit-card layer nobody owns. And the model providers are raising prices and gating their all-you-can-eat tiers on the way to their IPOs. The line item is growing faster than anyone's ability to explain it.
of CFOs have real-time, unified spend visibility
projected growth in AI spend as a share of revenue, 2025→2026
typical variance between AI budget and actual
Sources: Coupa Strategic CFO Report 2026; BCG executive survey, Jan 2026.
Every tool that tracks AI cost today was built for the people generating the spend — traces, tokens, latency. None of it survives contact with a month-end close. Meanwhile the questions on the other side of the table are getting sharper: Where is the AI money going? What is it returning? What will it cost next quarter? "We'll get back to you" is not an answer that scales.
One integration layer across every source — model APIs, subscriptions, embedded inference, cards and procurement. Automated tagging to team, project, product, and GL code. Then the outputs no engineering tool will ever build.
Every dollar of AI spend mapped, tagged, and reconciled — accrual-friendly, GL-coded, variance-explained. AI stops being the asterisk in your close and becomes a line item you can stand behind.
Usage-based costs don't behave like seats. Model the things that actually move your number — provider price changes, tier restrictions, product-driven inference growth — and forecast under scenarios instead of finding out in the invoice.
A meaningful share of your AI spend is R&D credit eligible — but only with substantiation. Arvata produces audit-ready documentation automatically, mapped to qualified research activities. For many customers, the recovered credit covers the product before it saves a single token.
Mapped to qualified research activities, substantiation attached.
When the report finds the waste — bloated contexts, retry loops, oversized models — you're not handed a chart and a shrug. Arvata deployed engineers go in and cut spend 20–40% with quality benchmarked before and after. Finance gets the savings. Engineering gets finance off its back. Everyone keeps shipping.
QUALITY: HELD ✓
Read-only connections to where AI spend originates — and to the finance stack where it gets reported. No SDK, no proxy, no latency tax.
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