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Reliable, self-improving enterprise AI
Maitai makes building reliable AI applications easy. We autocorrect faulty model output in real-time and automatically fine-tune models that learn from their mistakes. This means our customers get more reliable results immediately, and over time, they gain custom models built specifically for their application that only get better and faster. You wouldn’t hire an employee who doesn’t learn from their mistakes—so why use a model that doesn’t? Maitai is here to deliver the next generation of reliable AI inference.
Maitai presents itself as an enterprise AI platform focused on reliable, high-performance inference for production LLMs. It emphasizes fine-tuning, evaluation, and monitoring to enable confident deployment.
The platform supports fine-tuning, evaluation, and monitoring of production-grade language models to ensure reliability and performance in enterprise settings. It is positioned as an end-to-end solution for managing inference workflows with emphasis on reliability and high performance.
Who it’s for: Enterprises and organizations deploying large language models who require reliable inference, model fine-tuning, rigorous evaluation, and production monitoring.
Founder and CEO @ Maitai. Previous co-founder at Yew, led the AI engineering org at Presto (S10), and Big Bets team at Earnin. I spent most of my career getting AI applications from skunkworks to production, and then scaling them from there.
Founder and CTO @ Maitai. Previously a Tech Lead at Presto (S10), delivering CV based analytics and LLM powered voice ordering for the enterprise drive thru. A true jack of all trades, I have hands-on experience with infrastructure, software development, the machine learning stack, and everything in between.
Real-time autocorrections and load balancing to solve your LLM issues today + tailored models that improve over time.
Maitai provides a lightweight middlelayer between applications and LLM providers to improve reliability, latency, and cost through real-time evaluations, model health awareness, and passive incremental improvements tailored to an application. It aims to reduce outages, hallucinations, and compliance risks while enabling quick integration for teams using LLMs.
Interpretable AI models and agents
The LLM Eval and Observability Platform for AI Quality
