We built Oxlo.ai because we saw a growing problem as AI agents moved from demos into production.
When agents run continuously, usage becomes difficult to forecast. A successful agent does more than generate text. It reasons, calls tools, executes workflows, and serves real users. As adoption grows, infrastructure spend grows with it.
Over the last year, we ve watched AI move from simple chatbots to agents that can reason, call tools, execute workflows, and serve real users.
As founders, we noticed something interesting.
Most teams spent weeks comparing models, optimizing prompts, and building product features. Then they deployed to production and discovered a completely different challenge: cost predictability.
A successful AI application often becomes a victim of its own success. More users means more requests. More requests means more model usage. Before long, teams find themselves spending more time watching token consumption than building their product.
Oxlo.ai
