Maxim AI, which is developing an enterprise-grade evaluation and observability platform for AI application development, recently announced a $3 million funding round led by Elevation Capital.
The round also saw participation from marquee angel investors including founders of Postman, Chargebee, Groww, Razorpay, Media.net, among others.
With the infusion of capital, Maxim announced the General Availability (GA) launch of their platform, marking a significant milestone in their journey.
LLM, generative AI development slowed by current practices
Maxim said with the recent explosion of large language models (LLMs), the race to build and deploy AI applications has intensified.
Traditionally, software has built in a deterministic paradigm with standardized best practices for predictably testing performance and systematically improving products, tightly integrated into the development lifecycle, Maxim said.
Generative AI products, however, are getting built in a non-deterministic paradigm with unpredictable variability in quality and performance depending on factors including models, parameters, data or context, or simply the framing of questions, the company added.
Moreover, challenges around hallucinations, inaccuracies, safety, and output structure can lead to compromised user experiences and reputational risks for organizations. Maxim said organizations are resorting to non-scalable techniques, high-paid manual effort, and tediously slow iteration cycles as they test and ship their AI into production.
Software stack can evaluate throughout AI development process
Founded in 2023 by Vaibhavi Gangwar and Akshay Deo, Maxim aims to bring the best practices of traditional software development to streamline AI development workflows. Gangwar and Deo collectively bring over a decade of experience in building AI and developer-focused tools from Google and API development platform Postman.
Maxim’s infrastructure, which sits between the foundational model and application layers of AI stacks, is deeply focused on AI developers who comprise the traditional AI and machine learning engineering field, as well as the emerging class of backend engineers now leading AI development across organizations.
Maxim said its software stack can provide end-to-end AI evaluation across the development lifecycle: right from prompt engineering, pre-release, and post-release testing for quality and functionality, to data management and fine-tuning. The company said its automated evaluation platform can help teams ship products more than five times faster, and can also offer support for human evaluation as well as custom datasets and models.
“Even though we are seeing tremendous value creation in the AI hardware and foundational model stack, for the benefits to be realized meaningfully up the value chain, we need the products built on top of the generative AI stack to be trustworthy and reliable,” Gangwar and Deo said.
“However, today there is no standardization in the generative AI testing space unlike traditional software development. That’s where Maxim comes in: with our users, we have become this very core piece of infrastructure that is powering high quality AI development, streamlining team collaboration, and saving development teams hundreds of hours per month,” they added.
Funding to support expanded AI evaluation platform capabilities
“Maxim AI is addressing a crucial need in the evolving AI landscape,” said Akarsh Shrivastava, principal for Elevation Capital. “Their focus on creating a standardized, scalable evaluation infrastructure addresses a deep pain point for AI developers.
“As the complexity and diversity of AI use cases continue to rise, Maxim's platform will play a critical role in ensuring trustworthy and reliable AI systems,” Shrivastava added. “We are super excited to partner with Vaibhavi and Akshay as they build Maxim.”
So far, in private beta, the company has attracted multiple global customers who it said are at the forefront of the AI revolution. Looking ahead, Maxim said it is committed to expanding its platform capabilities, including proprietary domain-specific evaluations for quality and cybersecurity, as well as its data engine to accelerate reliable and scalable deployment of AI products.
The company said it plans to use the funds to expand its team and scale its operations to partner with more enterprises building AI products.
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