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Substrate receives  million in funding to bring the Lego brick approach to enterprise AI
Substrate receives  million in funding to bring the Lego brick approach to enterprise AI

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Substrate, a startup founded by tech veterans Rob Cheung and Ben Guo, quietly emerged from obscurity last week to launch its artificial intelligence development platform. The company also announced that it has raised $8 million in a funding round led by Lightspeed Venture Partners to grow its team and expand its product offerings.

Substrate aims to democratize AI by providing enterprises with a unified platform to build, deploy, and manage machine learning models and pipelines. Its flagship offering is an API that enables developers to build complex AI workflows by stitching together high-quality open-source models curated and optimized by Substrate.

The company believes its platform will make it significantly easier and more cost-effective for enterprises to harness the power of advanced AI capabilities such as Large Language Models (LLMs) and other generative AI techniques. This could accelerate the adoption of AI in industries ranging from content creation to business analytics to customer support.

Breaking down complex problems into manageable parts

“The main problems with integrating the current generation of AI, and LLMs in particular, are currently accuracy, cost and latency,” explained Rob Cheung, co-founder and CEO of Substrate, in an interview with VentureBeat. “Substrate addresses all three problems by allowing developers to break down a large, complex problem into many smaller, more constrained problems that are easier to solve.”


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Cheung drew an analogy to how Google Search works behind the scenes, analyzing queries using 15 to 20 different machine learning models working together. “All of that gets squashed into one giant prompt. You might get good answers 10% of the time, but 90% of the time you’re really in the dark about how that giant prompt is going to steer you,” he said. “If you break it down and have a good way of creating a broken-down description of the problems, that solves a lot of the accuracy problems.”

While tech giants like Google have built extensive infrastructure to optimize and orchestrate large numbers of ML models, most companies lack these capabilities. “We think it makes a lot of sense to centralize all the performance optimization work in one place and offer it as a service, because that’s what people really want,” Cheung said. “One of our big customers, Substack, isn’t actually interested in running machine learning infrastructure at all. They want Lego bricks to build their ML workload and just make it work.”

Curated models and better abstractions increase productivity

Substrate co-founder Ben Guo said the company’s experience with early customers like Substack, which used the platform to generate summaries and topic categories for blog posts, showed the value of its approach. “They can use all of their models in one place running on a cluster, which enables much faster execution speeds for fairly large workloads, as well as lower costs and better reliability,” he explained.

In addition to the performance benefits, Guo believes Substrate’s curated, plug-and-play models will also appeal to companies that don’t want to wade through the ever-expanding landscape of open-source AI. “One of the things people want is for us to read the literature, cut through the noise, and pick the most interesting and useful models as they come out,” he said.

Substrate also aims to create a better developer experience by providing simple abstractions and templates for common enterprise use cases. “We’re taking a step back, looking at the landscape, and trying to figure out the Platonic ideal for these abstractions, which I don’t think anyone is really doing right now,” Guo told VentureBeat. “It’s similar to what I learned at Stripe — there’s a lot of hidden value in creating very simple abstractions,” such as enabling a payment integration in just seven lines of code.

Integrating the Cloud Platform Playbook into Enterprise AI

As large language models and other AI building blocks become more powerful and accessible, platforms like Substrate could play a key role in helping companies translate these raw capabilities into real-world applications and business value. With a more abstracted, full-stack approach to AI development, Substrate aims to do for machine learning what cloud platforms have done for general computing – make it easier and more economical for companies to develop and deploy powerful software.

The $8 million funding round will enable Substrate to expand its platform, grow its team, and ramp up its go-to-market efforts to reach more enterprise customers. With experienced founders and strong early success, the startup appears well-positioned to become a major player in the rapidly evolving world of enterprise AI.

By Aurora