San Francisco-based Plato has raised $16 million in Series A funding to develop simulated workplaces where AI models can practice completing business tasks before operating inside real companies.
Nava Ventures led the round, with participation from Gradient Ventures, Transpose Platform and Mustafa Suleyman, the CEO of Microsoft AI.
The financing brings Plato's total funding to $19 million, following an earlier seed round led by Gradient Ventures.
Founded in 2025 by Robert Farlow and Pranav Putta, Plato is addressing a problem that becomes increasingly important as AI systems move beyond generating text and begin executing actions across business software.
Why AI Agents Need Training Environments
Large language models are primarily trained using extensive collections of text, images, code and other data.
But an AI agent working inside a company must do more than generate answers.
It may need to navigate software, reconcile records, process orders, update customer information or complete a sequence of actions involving multiple applications.
These tasks require understanding not only what an instruction means, but how to execute it while respecting business constraints.
An agent could technically complete a purchase while exceeding an approved budget.
It might update a customer record correctly but accidentally modify unrelated information.
A model could also appear successful in a controlled benchmark while failing when a real workflow becomes more complicated.
Plato is building environments where developers can test these situations without exposing production systems to risk.
How Plato's Simulations Work
The company recreates software applications and operational processes inside controlled digital environments.
AI developers can place agents into these simulations, assign tasks and measure their performance.
For example, a simulated procurement workflow could require an agent to identify a supplier, check pricing, confirm an available budget and prepare a purchase.
The environment can track whether the agent completed the required steps correctly.
If an agent makes a mistake, the simulation can be reset and the task attempted again.
This repeatability is especially important for reinforcement learning.
In reinforcement learning, models improve through repeated interactions and feedback.
Providing those interactions through real financial accounts or customer databases would be expensive and potentially dangerous.
Simulations allow researchers to generate experience without repeatedly affecting real business operations.
What Makes a Good AI Training Environment?
Plato is developing simulations for customer support, human resources, finance, accounting and healthcare administration.
The company works with domain experts to reproduce the tools and decisions involved in those professions.
That human input is important because a realistic workplace simulation requires more than replicating a software interface.
It must also reproduce business rules, dependencies and unexpected situations.
A financial task might require approval when a transaction exceeds a particular amount.
A customer support workflow may need to distinguish between a routine request and a situation requiring escalation.
Without these constraints, a model could learn shortcuts that produce a high benchmark score without performing the underlying work properly.
Plato says its evaluations examine both the result of a task and the actions taken to reach it.
The company is also developing methods to detect reward hacking, where models exploit weaknesses in an evaluation system rather than completing the intended objective.
Who Founded Plato?
Plato was co-founded by Robert Farlow, its CEO, and Pranav Putta, its chief technology officer.
Farlow previously built an annotation platform at Yutori to support the development of computer-use AI models.
Putta has experience in computer vision, robotics and reinforcement learning, including work training computer-use systems at MultiOn.
Their backgrounds influenced Plato's focus on the data and evaluation infrastructure needed for autonomous software agents.
Competition in AI Agent Training
Plato operates in a growing market for AI training environments, evaluation systems and reinforcement learning infrastructure.
Companies including Scale AI, micro1 and specialized AI evaluation providers are developing datasets and environments designed to improve model performance.
Its differentiation is the focus on realistic workplace processes created with domain experts.
The company is not developing a general-purpose enterprise assistant for businesses to deploy directly.
Instead, it aims to provide the environments and training experiences used by the AI laboratories building those assistants.
That distinction places Plato within the infrastructure layer of AI development.
Commercial and Technical Risks
Simulation-based training depends on the quality of the environment.
A model may perform well inside a simplified application without handling the unpredictable behavior of real enterprise systems.
Software interfaces change, business policies vary and unexpected situations can arise.
Plato must show that performance improvements achieved in its environments translate into better results when agents operate in production.
It must also demonstrate that its evaluations can distinguish genuine progress from models learning to exploit the tests.
The company has not publicly disclosed customer revenue, the number of AI laboratories using its environments or independent measurements of training effectiveness.
What Comes Next?
Plato will use the financing to expand its library of simulated workplaces and compensate professionals who recreate their actual jobs as training environments.
The company also plans to collect additional experience by deploying agents on real tasks.
Plato's opportunity is to become a training and evaluation infrastructure provider for the next generation of AI agents. Its decisive test is whether experience gained inside simulations reliably improves performance in real workplaces.