Actualyze AI Secures $7M Seed to Help Enterprises Control AI Spending

Actualyze AI closed a $7 million seed round Monday for its platform designed to give enterprises unified control over artificial intelligence usage. The company emerged from stealth mode with the funding announcement.

The platform positions itself between an organisation's workforce, applications, agents, and every AI model in use. It delivers a single point of control for governing, securing, operating, and optimising AI requests across the enterprise.

Actualyze integrates with any OpenAI-compatible model, the broader ecosystem of AI client tools, and third-party platforms. The architecture ensures every request follows a governed path, enabling companies to adopt and scale AI without losing visibility or control.

It's clear that AI has become a new layer of the enterprise stack, said Rafi Khardalian, CEO and co-founder of Actualyze AI.

He explained that while a model call resembles any other API request — complete with a key, an SDK, and a monthly invoice — that similarity creates a trap. Traditional systems governing an organisation's request traffic can authenticate a model call and count it, but they cannot read the prompt inside, detect data leakage, determine optimal routing, or attribute costs to specific teams.

So anyone with a key can call a model, and all that spend pools into one bucket with no visibility into who spent it or accountability for it, Khardalian said. > Agents raise the stakes, fanning a single task into dozens of autonomous calls. That's the problem we built Actualyze to solve.

The launch arrives as enterprises shift toward much stricter oversight of AI expenditure. Companies spent the past two years in a phase of tokenmaxxing — pushing employees toward the largest AI models and heaviest usage, treating consumption as a proxy for progress.

That era is ending after two years of unchecked growth. The approach worked while AI spending remained small enough to absorb without scrutiny. It no longer qualifies as immaterial.

Enterprise software historically relied on annual licences and seat-based pricing that finance teams could forecast with reasonable accuracy. AI disrupted that model entirely, introducing pricing based on tokens, compute cycles, and API calls.

New tools are emerging to address the gap. Ramp introduced AI Token Spend Management last month, providing finance teams a unified dashboard to track, allocate, and control AI spending across providers including OpenAI, Anthropic, Gemini, and Cursor.

The company reported that AI token spend across its customer base surged 20.7x since June 2025, underscoring the scale of the cost management challenge facing enterprises.