Relanto, an AI-first global advisory and services company, announced the launch of R-TokenomIQ™, an enterprise AI economics platform designed to help organizations understand, govern, secure, and optimize AI consumption as it scales across the business.

As AI becomes embedded across applications, workflows, copilots, and agents, enterprises are managing a new and rapidly growing category of enterprise spend.

Traditional software economics are largely built around predictable licenses, subscriptions, and capacity commitments. AI follows a different model, with spend increasingly driven by ongoing consumption across prompts, models, workflows, agents, and infrastructure. At enterprise scale, leaders need visibility not only into overall spend, but into where AI investment is flowing, who is accountable for it, and what value it is producing.

Today, most enterprises piece that visibility together from model-provider dashboards, cloud billing consoles, gateways, observability tools, and security platforms, each showing a fragment of the picture. R-TokenomIQ™ brings those signals into a single system built around how AI is consumed, governed, and translated into business outcomes.

“AI is rapidly becoming a significant enterprise operating expense, yet the management discipline around it is still catching up,” said Rajan Gaur, Chief Executive Officer of Relanto. “The next phase of Enterprise AI will not be defined by how much intelligence organizations consume, but by how effectively they convert that consumption into measurable business value. R-TokenomIQ™ gives leaders the clarity and control to turn AI investment into disciplined, accountable spend, driving greater predictability, sharper decision-making, and stronger returns.”

R-TokenomIQ™ brings together five core capabilities:

• AI Spend & Usage Monitoring - Track tokens, latency, model activity, and spend across LLM providers, cloud platforms, and coding agents.
• Spend Attribution & Accountability - Attribute AI usage and cost across teams, projects, departments, and users.
• Policy & Budget Governance - Apply budgets, quotas, model policies, API controls, and organizational guardrails.
• AI Value Optimization - Improve efficiency through model recommendations, intelligent routing, forecasting, and prompt optimization.
• Security & Risk Oversight - Manage access, audit activity, sensitive content, and supported AI chat tool usage.

“The AI race is shifting. The advantage is no longer who has access to the most compute, but who can extract the most value,” said Yeshwant Nayak, Chief Operating Officer of Relanto. “R-TokenomIQ powers that shift by turning fragmented AI consumption and spend into greater visibility, governance, and measurable enterprise outcomes.”

Traditional financial governance often begins with a budget and ends with a report. With AI, that approach alone is not enough. Usage and spend can shift continuously across people, applications, workflows, and agents, often before those changes are visible in an invoice or financial report.

As AI becomes a larger and more dynamic part of enterprise operations, managing its economics will become as important as managing the technology itself. R-TokenomIQ™ provides the visibility, governance, and optimization needed to understand where AI investment is going, how it is being consumed, and what value it is creating. Together, these capabilities bring greater discipline and accountability to enterprise AI economics.