In the AI arms race, Metal aims for enterprise dominance with $2.5 million war chest

Metal, an artificial intelligence startup focused on enterprise applications, announced today that it has raised $2.5 million in seed funding led by Swift Ventures with participation from Y Combinator and Chapter One. The funding will be used to expand Metal’s platform for building AI-powered applications and to provide increased support for large enterprise customers.

Taylor Lowe, CEO and co-founder of Metal, discussed the company’s mission and the impact of their technology during an exclusive interview with VentureBeat. “The success metric for us are live applications that solve problems that can be used by enterprises that can improve people’s lives, and improve the way that people work,” said Lowe. “We want to change the narrative that there’s all this experimentation; we want to see real-world deployments.”

Metal offers a fully managed platform for developing AI applications using natural language processing (NLP) and large language models (LLMs). The platform handles complex infrastructure like data transformation and storage, allowing developers to focus on creating applications.

“Engineers don’t have to handle data transformation, indexing, indexing pipelines, storage, all that stuff that’s boilerplate and infrastructure, things that you need to power an application, but where you’re not getting the value out there, you’re not learning about how customers would use and interact with this stuff. We want to remove as much of that as possible,” said Lowe.

Helping developers build faster and easier

Lowe said that Metal’s platform helps developers build applications using LLMs faster and easier than building their own solutions or using off-the-shelf chatbot builders. He said that Metal’s platform removes a lot of complexity and infrastructure that developers don’t need to handle.

“We’re really at the beginning of this installation period. So for 99% of organizations, this is still so new, business leaders are just trying to get their heads around what this technology does, what it means for them,” Lowe said. “We want to work directly with these teams to not only push the product, but to prove the value of the technology.”

Metal also offers hands-on assistance and consultation for enterprises that want to experiment with and deploy AI and LLM applications across their organizations. The company works directly with business leaders to validate the value and use cases of this technology, as well as providing observability into the application’s usage and performance.

Fresh capital to deploy toward building the platform

Lowe said that Metal plans to use the seed funding to focus on three areas: building out the core platform and its open-source project, investing on support for the enterprise, and hiring more talent for its team based in Williamsburg, New York.

Some of the use cases that Metal enables include information retrieval, personalization and insights. Lowe said that Metal’s customers span across various industries, such as real estate, financial services, marketing and PR agencies. He shared some success stories where Metal has made a significant impact for its customers, such as Lastro, a real estate company in Brazil that built a chat application with Metal where prospective renters or buyers can ask questions about the market.

“They kind of just started this [chatbot for prospective renters and buyers] as an experiment, and then it very quickly [became popular],” Lowe said. “They have really good numbers in terms of how many users are interacting with this index querying it every day.”

“What this technology is really good at is basically translating unstructured data, things like an RFP, a slide deck, meeting notes, all of this sort of stuff that floats around in the ether within an organization—but that’s not really put to use,” Lowe said. “If you think about it, if you’re an enterprise, you’re hiring all these people that take notes and drive projects forward, they’re putting in all of this work to create digital assets — and then [the assets] just kind of go away.”

Meeting the growing demand of LLMs in enterprise data

Lowe also said that Metal’s bet is on developers to drive the innovation and transformation in the AI and enterprise data space. He said that Metal wants to empower developers to build applications that solve real-world problems and create value.

“The success metric for us are live applications that solve problems that can be used by enterprises that can improve people’s lives improve the way that people work,” Lowe said. “We want to change that narrative of there’s all this experimentation we want real world deployments.”

Metal is one of the startups that are tapping into the growing demand and potential of AI and LLMs in the enterprise data space. According to Deloitte’s State of AI in the Enterprise 2022 report, 79% of respondents say they’ve fully deployed three or more types of AI compared to just 62% in 2021. 

Metal faces stiff competition from other platforms that offer similar capabilities, such as Hugging Face, which provides an open source library for NLP models and datasets; Primer.ai, which helps enterprises analyze large volumes of text data; or OpenAI Codex, which is a system that can generate code from natural language commands.

However, Metal differentiates itself by focusing on providing a fully managed service for developers who want to build applications using LLMs without having to deal with complex infrastructure or data pipelines. Metal also prides itself on being practical and customer-oriented, working closely with enterprises to validate and deploy their AI and LLM applications.

“We’re not interested in the hype, we’re interested in solving problems and proving the value of this stuff,” Lowe said. “We kind of see ourselves as practical in that sense.”