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Sapient Intelligence, Singapore's first foundation model AI startup, has announced the successful closure of its seed funding round, raising $22 million at a valuation of $200 million.

Backed by prominent investors including Vertex Ventures, Sumitomo Group, and JAFCO, the company is hoping to carve a distinctive path in AI development, addressing what it sees as fundamental shortcomings in GPT-style models.

"The goal of the startup, really, is to make a new generation of foundational model architectures to solve, like really complicated and long horizon reasoning tasks that's kind of really that are really challenging for large language models, especially for GPT architectures, to solve," said co-founder and CEO Austin Zheng in a recent interview with VentureBeat conducted over video chat.

New architectures beyond traditional Transformers

Traditional GPT-style models rely on autoregressive methods, which generate predictions by building sequentially on prior outputs.

While effective for general tasks, this approach struggles with multi-step reasoning and complex problem-solving.

"With current models, they're all trained with a autoregressive method, and with that, the benefit is it's easier for the model to converge on general task," Zheng explained. "So it sounds really smart, so it can solve a lot of different tasks. It has a really good generalization capability, but it's really, really difficult for them to solve a sub, complicated and long horizon, multi-step tasks. And that's kind of where hallucination comes in," Zheng said.

Sapient's answer is a novel model architecture inspired by neuroscience and mathematics, blending transformer components with recurrent neural network structures and mimicking how the human brain works.

"The model will always evaluate the solution, evaluate options and give yourself a reward model based on that," Zheng said. "And also the model can continuously calculate something recurrently until it gets to a correct solution. With that, our our agent will be able to deploy to an environment in an enterprise or production environment, and continuously learn and improve ourselves by trial and error and learn to be an expert on the existing code base."

This design underpins the flexibility and power of Sapient's models, enabling them to tackle a broad range of tasks with precision and reliability.

The company's innovations are reflected in benchmark performance.

"The first benchmark we use is actually Sudoku," Zheng told VentureBeat. "Right now, our model is the best performing neural network in terms of solving Sudoku on the market -- 95% accuracy without using intermediate tools and data."

According to Zheng, while other leading models needed to train on intermediate steps to solve the popular numeric ordering puzzle, Sapient only provided the model with unfinished Sudoko boards, the rules, and the final solutions, and must infer on its own how to solve them through trial and error.

Similarly, Sapient's models have excelled in tasks like two-dimensional navigation and complex mathematical problem-solving, consistently outperforming competing approaches.

Training these models is another area where Sapient distinguishes itself. "Unlike traditional models that require vast amounts of high-quality, step-by-step data, our approach needs only question-and-answer pairs. This significantly lowers the barrier for training complex models," Zheng said.

By leveraging synthetic data, Sapient reduces the dependency on curated datasets, creating scalable and efficient training pipelines.

Practical applications: from code to robots

Sapient's initial focus is on real-world applications, starting with enterprise coding and robotics.

Its autonomous coding agents aim to revolutionize how businesses manage their software development and maintenance needs.

The company is already deploying an autonomous AI coding agent in Sumitomo's enterprise environments to learn the company's codebase and ultimately, begin maintaining and contributing to it.

Sapient aims to offer a similar service to other enterprise clients, what Zheng describes as "smart and tailored AI employees and AI software engineers that can help them maintain, update and also grow the existing tech stacks."

Unlike Cognition's Devin, powered by GPT-4o, Sapent believes its coding AI agents will be able to work autonomously -- without any human guiding the process or troubleshooting issues, save for supervisors checking over the work before it is pushed live.

The company is also advancing embodied AI, designing models that enable robots to interact, learn, and adapt in real-time.

"There are only a handful of startups working on understanding of environment, and also planning of options and tasks, and understanding what kind of tasks are possible -- also continuous, improving itself on understanding the environment, understanding the problem, and understanding the use cases," Zheng pointed out. "This will be our main focus for the next 1-2 years."

A global vision

Sapient is setting itself apart not just through technology but also its global and inclusive approach.

"There are very few AI startups at a foundational model level outside of China actually led by Asian founders," Zheng noted. "We really want to position ourselves as an international and research-oriented organization. But also, we want to be one of the first, few Asian-led international research organizations that are solving really, really challenging problems, and we're seeing that coming to fruition as well."

With offices in Singapore and plans for the Bay Area, the company is building an AI research lab to bring together diverse perspectives and talent.

Its team reflects this ethos, comprising scientists and engineers from leading institutions like DeepMind, Anthropic, and Microsoft AI.

This diversity, combined with strong partnerships with Japanese investors like Sumitomo Group, positions Sapient as a unique player in the global AI ecosystem.

Targeting individuals and enterprises

Sapient's long-term vision is ambitious, targeting technology that can be applied with equally useful results to individuals and enterprises.

"The goal at the very end will be to build a truly generalized agent that can actually solve a day-to-day task for our users -- an 'all agent solution' for a personal assistant and for solving all your tasks..that's where we are in terms of our technological goal and also our direction," Zheng said.

This includes future public-facing products like autonomous coding agents and general-purpose personal assistants.

For now, Sapient is focused on refining its technology and delivering enterprise-grade solutions. Pricing models are still being explored but may include licensing, subscription fees, or task-based charges tied to successful completions.

As Sapient scales its operations and capabilities, it remains a company to watch in the rapidly evolving AI landscape.