About - P10Y

Drive Software Engineering Productivity with Objective Data

At P10Y, we aim to unlock the full potential of software engineering productivity by embracing data-driven precision. Say goodbye to guesswork and subjective metrics.

Our revolutionary approach focuses on quantifiable measurements based on actual work output (code shipped). With advanced data analytics, we empower teams to make informed decisions, increasing efficiency, accuracy, and project success.

Join us in this transformative journey and experience a new era of productivity in software engineering!

Effortless Internal and Industry-Wide Performance Benchmarking for Your Organization

Our algorithm directly measures actual code output, providing a universal and unbiased metric for comparison across industries, independent of programming language, tech stack, or service type.

P10Y empowers organizations to quantify and compare software engineering productivity with unprecedented accuracy.

Founding Team Driven by a Passion for Software Engineering Productivity

With a profound understanding of the industry's challenges and the crucial need for objective measurement in software output, our founding team has embarked on a mission to revolutionize the industry and provide reliable solutions for software engineering productivity.

Simon

Seasoned engineering leader and serial entrepreneur (3x founder) who successfully built a 250-engineer software development agency and served as CTO at a unicorn (>$1B+ exit).

Yegor

Stanford serial entrepreneur (3x founder) and researcher. Former Chief of Staff to the CEO at one of the world’s largest companies, responsible for digital transformation strategic initiatives.

A Solution Based on Rigorous Scientific Research

A Solution Based on Rigorous Stanford Research

At P10Y, our solution is innovative and rigorously grounded in academic research, incorporating scientifically validated findings. Drawing from studies conducted at a leading private university in the Bay Area, our core principles revolve around software engineering productivity and machine learning.