Celeri

Celeris-1 is a low-latency, general-purpose language model platform and a diffusion model designed to deliver frontier-level intelligence at dramatically higher speed. Instead of generating one token at a time like traditional autoregressive models, Celeris uses a diffusion-based inference architecture that enables parallel generation and response times measured in milliseconds. On its published MMLU-Pro benchmark, Celeris-1 reaches 75.9 accuracy with a 158 ms median response time and 1,664 output tokens per second, placing it within a few points of frontier models while running more than 10x faster. The model is exposed through an OpenAI-compatible API, so developers can point existing SDKs and clients at Celeris with minimal code changes. Streaming is enabled for interactive applications, with responses as low as 24 ms and no buffering or batch delay.

Applicants are falsifying financial documents like paychecks and bank statements and submitting them in their applications. In fact, 1 in 8 rental (12%) applications contains some type of fraud. The digitization of the rental application process has led to rampant application fraud. In the past year, 85% of property managers have experienced application fraud, which accounts for an estimated 25% of evictions. At $7,500 or more per eviction, this is an expensive problem. The best way to avoid evictions is to thoroughly screen tenant applications, yet it’s easier than ever for tenants to alter documentation, such as pay stubs and bank statements, so they can fraudulently qualify for units. Snappt flags those fake documents helping properties to greatly reduce costly evictions. Detect more fraudulent documents with advanced data-driven forensics methods. Streamline the application and screening process so you can make informed decisions quickly.