Thread AI Emerges From Stealth With Composable Infrastructure Platform

Source: Businesswire

Thread AI, a new company founded by former Palantir AI Product and Engineering leads Angela McNeal and Mayada Gonimah, emerged from stealth today to announce the launch of its composable AI infrastructure platform, Lemma. This AI workflow solution brings together disparate or previously incompatible systems into one observable, robust, and secure place so that enterprises can effectively implement and execute with AI.

With Thread AI’s Lemma platform, companies can easily build mission-critical, automated workflows while seamlessly incorporating important constraints, like cost and human-in-the-loop handoffs. This means companies can focus on investing in proprietary logic and accelerating feature delivery without wasting time on platform and architecture decisions that slow development and execution. Thread AI is already working with some of the leading brands in luxury hospitality, digital marketing, public safety, and financial services.

“Enterprises are eager to adopt AI but struggle navigating a fragmented landscape of tools and addressing critical security and governance concerns,” said Angela McNeal, co-founder of Thread AI. “Today, enterprises that are trying to leverage AI for workflow automation with a human-in-the-loop component typically are limited to two bad choices: either build the supporting infrastructure from the ground up, which is time-intensive and challenging, or purchase an application layer service with a single solution to a specific workflow, which doesn’t scale. Maya and I knew we could develop something better.”

It takes complex and purpose-built infrastructure to seamlessly connect different AI data-models, to orchestrate across dozens of different systems and authentication policies, and to provide concise interfaces for the right guardrails, all of which require specialized knowledge and significant time to build in-house. Lemma solves for this, enabling enterprises to build scalable, safe, and robust AI-powered workflows without having to develop and maintain complex infrastructure across MLOps, ETL, and Orchestration stacks.

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