Introducing Inference platform: Monitor, train, and deploy self-improving AI models

Inference platform is a public-beta full-stack system for improving production AI applications through integrated traffic monitoring, evaluation, supervised fine-tuning, and deployment. Designed specifically for deployed AI systems rather than general research, it captures real LLM requests through an inference gateway compatible with OpenAI- and Anthropic-style providers, then converts that traffic into training and evaluation datasets. Its workflow establishes baseline performance using LLM-as-a-judge evaluations, applies preconfigured training recipes and infrastructure, and deploys resulting specialized models to dedicated GPUs or customer-controlled environments. The company claims these models can match or exceed frontier-model quality at up to 95% lower cost, citing production uses in coding, extraction, calorie estimation, and research-agent applications. In contrast to reinforcement-learning approaches that rely on simulated environments and complex reward design, the platform argues that production data provides a more accurate basis for optimization, and it is offering free training and deployment during its beta period.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.