We are redefining the economics of intelligence. UnieAI achieves superior reasoning via Test-Time Scaling, while slashing token costs through Kernel-Level Optimization.
To achieve AGI-level reliability in enterprise domains, simple generation isn't enough. Models need to 'think' before they speak. This is Test-Time Scaling—trading inference time for intelligence.
Normally, this makes AI slow and expensive. But UnieInfra changes the equation. By optimizing the underlying compute kernels, we dramatically increase throughput.
Our platform enables Agentic Context Engineering (ACE) to perform complex reasoning loops, supported by an infrastructure that makes heavy compute economically viable.
"We treat Intelligence as a function of Compute Time, and Cost as a function of Throughput efficiency."
We don't just prompt; we engineer the reasoning process. Using Test-Time Scaling, our agents decompose complex domain problems, verify facts, and self-correct in real-time. This ensures deep stability and expert-level accuracy that standard 'one-shot' generation cannot match.
To support heavy reasoning, we rebuilt the inference stack. Utilizing Triton kernel optimizations, parallel scheduling, and industrial-grade Speculative Decoding, we maximize GPU utilization. The result is significantly higher throughput per unit of compute—lowering your cost per token.
A vertical integration of Agentic Logic and High-Performance Computing.
Implements Agentic Context Engineering. It manages the 'System 2' thinking process, orchestrating recursive loops for domain knowledge reinforcement.
The foundation. Powered by custom Triton kernels and Speculative Decoding, delivering the high throughput required to run agentic workflows at scale.
The control plane. Allows enterprises to configure reasoning depth (Test-Time Scaling) against budget constraints in real-time.