Why We Invented Tikoly.com
Modern AI products no longer make one call to one model. A single task fans out into dozens of chained requests — drafting, tool use, review, recovery — across multiple models and providers. That is how software is built now, and it quietly broke the traditional LLM API model in three ways.
Costs became unpredictable. Per-token pricing was designed for a single request, not for a workload that multiplies requests on its own. Teams stopped being able to answer a simple question before it happened: what will this run cost?
Quality became unaccountable. When an agent misbehaves, the first question is what did it call, what did it choose, and who allowed it. Most stacks cannot answer with evidence.
Operations became fragile. A single provider outage, rate limit, or bad model choice could stall an entire product workflow, with no policy in place to decide what should happen instead.
We built Tikoly.com because none of that is optional for an enterprise. Agentic AI needs the same discipline the rest of the stack already has: policy before spend, evidence after action, and routing that keeps working when something upstream does not.
What Tikoly.com Is
Tikoly.com is a managed LLM service built for robust agentic operation. Every request is handled according to your policy — the right model for the job, cost checked before it is spent, and a complete record kept after the fact. Instead of adapting your product to the economics and failure modes of raw model APIs, you point your agents at Tikoly and get predictability, auditability, and resilience as service characteristics, not as an in-house engineering project.
What Private Alpha Includes
Private alpha members get early access to the service with onboarding support from the team that built it, generous usage for evaluation in real workloads, and a direct line that shapes the roadmap. Seats are deliberately limited for this phase.
If your team is running agents in production — or plans to — we would like to talk. Request access at tikoly.com/ask.