NeurFly Raises $5.5M Seed Round Led by Accel Partners to Democratize Neural Network Automation
Boston-based AI startup NeurFly has raised $5.5 million in a Seed Round led by Accel Partners, marking a significant milestone in the company's mission to make production-quality neural network automation accessible to every engineering team.
Today, we are thrilled to announce that NeurFly has closed a $5.5 million Seed Round led by Accel Partners. This investment will accelerate the development of our neural architecture search (NAS) platform, expand our engineering team, and help us reach the thousands of companies that are struggling to build and deploy neural network models at the pace their products demand.
Since our founding in early 2024, we have been working on a deceptively hard problem: the gap between the state of the art in neural network research and what most engineering teams can actually build and deploy. Top AI labs can run sophisticated architecture search experiments, leverage hardware-aware compilation, and automate hyperparameter optimization at scale. The rest of the world writes the same ResNet-50 baseline and calls it a day.
Why Neural Network Automation Matters Now
The demand for AI-powered products has never been higher. Every company — from healthcare startups to logistics giants — is trying to build applications that rely on neural networks. Computer vision for quality control. NLP for customer support automation. Time series forecasting for demand planning. Recommendation systems for personalization.
Yet the tools for building these models have not kept up with the demand. Hiring ML PhDs is expensive and competitive. Hand-crafting architectures takes weeks. Hyperparameter tuning is a dark art. Hardware optimization requires specialized knowledge that most teams simply do not have.
NeurFly solves this problem by automating the hardest parts of neural network development. Our platform uses differentiable architecture search and multi-objective optimization to find optimal model architectures for your specific data, task, and hardware — automatically, in hours instead of weeks. The result is models that match or exceed what expert ML engineers would build by hand, at a fraction of the time and cost.
What This Funding Enables
The $5.5 million Seed Round from Accel Partners will be put to work in three areas:
Platform Expansion: We will invest significantly in expanding NeurFly's core architecture search capabilities, with particular focus on transformer-based NAS for large language model fine-tuning and multi-modal model design. We are also building out our hardware-aware optimization layer to support the latest GPU and NPU architectures.
Team Growth: We are hiring across engineering, research, and customer success. If you are a machine learning engineer, infrastructure specialist, or AutoML researcher who wants to work on problems at the frontier of applied AI, we want to hear from you. See our open positions at contact@neurfly.com.
Customer Partnerships: We are launching a structured early access program for engineering teams who want to pilot NeurFly in production. Early access customers will get hands-on support from our team and influence the product roadmap directly.
A Word from Our Investors
"NeurFly is addressing one of the most significant bottlenecks in enterprise AI adoption," said a partner at Accel Partners. "The ability to automate neural network design and optimization means that any engineering team — regardless of their ML expertise — can build production-quality AI applications. Elena and her team have deep expertise in both the research and the engineering required to make this work at scale. We are excited to partner with them as they grow."
From the CEO
"We founded NeurFly because we believed that the divide between frontier AI research and what most engineering teams can build is solvable — with the right software," said Elena Volkov, CEO and co-founder of NeurFly. "This investment from Accel Partners validates that belief and gives us the resources to move much faster. Our goal is to make neural network automation as accessible as cloud computing itself — something every team uses without needing to understand the complexity underneath."
NeurFly's platform currently supports PyTorch, TensorFlow, and JAX, and can optimize models for GPU, TPU, and edge deployment targets. Customers have reported a 90% reduction in architecture design time and 300% improvement in model training efficiency compared to their previous manual workflows.
To learn more about NeurFly or to request early access to the platform, visit our contact page or reach out directly at contact@neurfly.com.