OPTIMAZE helps businesses connect AI infrastructure costs to business outcomes, giving CTOs, CFOs, and engineering teams a real-time view of which experiments, products, features, and teams are efficient, profitable, or trending in the wrong direction. Most organizations face the same problem: their AI bill grows faster than their ability to explain it. Resources are tagged inconsistently, cost data is stale by the time it reaches decision-makers, and FinOps recommendations sit in engineering backlogs that never get actioned, just like with cloud. The result is a finance team that can't contextualize the bill without manual intervention, an engineering team that can't prioritize optimization, and a CTO who can't answer the CFO's most basic question: is our AI spend generating business value? OPTIMAZE Economics solves this from start to finish. Agentic cost attribution automatically maps every dollar of AI spend to the right cost owner - team, product, feature, or cost center - continuously and in real time, with up to 90% less manual effort than cost attribution approaches. Clean, granular, accurately attributed cost data is the prerequisite for everything else: unit economics, benchmarking, anomaly detection, and commercial optimization. From that foundation, OPTIMAZE delivers real-time unit economics - cost per user, tokens per feature, revenue per AI experiment - so that every team in the organization has the financial context to make better decisions. Engineers understand the cost implications of their model choices. Product managers can weigh the model cost of a feature against its business value. Finance leaders get token cost reporting in business language, not service-level billing aggregates and shared service catch alls. Conversational AI decentralizes that financial intelligence further. Rather than routing every cost question through a central FinOps analyst, any team member can query their cost data in plain language and receive answers grounded in verified attribution data, instantly. This self-service model reduces FinOps overhead significantly while improving the speed and quality of financial decision-making across the organization. OPTIMAZE integrates natively with OpenAI, Anthropic, AWS (including Bedrock), Azure, GCP (including Vertex), Snowflake, and Databricks, bringing cloud infrastructure, AI token costs, and data platform spend into a single economics framework. Predictable pricing ensures customers get increasing value from the platform, opposed to adding to their overhead through a percentage of their cloud bill. A leading financial institution uplifted their cost attribution by 40% in an afternoon, reduced FinOps overhead by 50%, and achieved 20% year-on-year savings through economics management and governance. They regularly achieve up to 90% improvement in time spent on administrative tasks like tagging and reporting.