Maitai

Total raised$500.0K
StageSeed
Founded2024
HQRedwood City, United States
IndustriesAI/MLSaaSEnterprise SoftwareCloudInfrastructure

About Maitai

Maitai is an enterprise AI company that manages the LLM stack for enterprise companies. It aims to enable the fastest and most reliable inference in enterprise AI. The company is growing fast and is described as well-capitalized, with top YC startups and public companies relying on Maitai to manage their models and agents. Maitai positions itself as well-positioned to capture the enterprise AI market and describes the future of enterprise AI as mosaics of small, domain-specific models powering powerful, responsive agents. Its mission is to help companies build the world’s fastest, most reliable AI agents for real-time applications.

Company at a glance

Location
Redwood City, United States
LinkedInLinkedIn

Location

Life at Maitai

Frequently asked questions

Companies similar to Maitai

Ambi

Ambi is building the interface for the AI era - software and hardware that move at the speed of thought. We're creating a living memory system that turns conversations, meetings, and daily signals into one connected knowledge base, across phone, watch, and desktop. Backed and early-stage, headquartered in San Francisco with hybrid flexibility - this is a ground-floor seat on category-defining hardware.

AI/ML11-50
Arbio

Arbio, headquartered in Berlin, Germany, is building an AI-native property management platform for short-term rentals across Europe. It currently manages over 1,000 apartments and operates real properties in Berlin, Hamburg, Vienna, and Leipzig, with plans to expand to additional European cities. The company has raised a $36 million Series A funding and maintains an engineering team of five engineers. Arbio’s platform uses AI to observe, decide, and act on real-world property operations with humans in the loop, enabling AI-assisted management of short-term rental properties across Europe.

AI/ML51-200
Arga Labs

Arga Labs provides isolated sandbox environments equipped with API twins of third-party services, enabling developers and organizations to safely test and train AI agents without risking their production systems. By offering a controlled testing ground that mirrors real-world APIs, the company allows teams to experiment with AI implementations, validate workflows, and refine agent behavior in a risk-free setting. This approach addresses a critical need in AI development, where testing in live environments can introduce costly errors or security vulnerabilities.

AI/ML1-10