Why Quotient
Software is now built by humans and agents, on an operations layer designed before AI. Quotient guides engineering organizations into an AI-native SDLC, measuring what slows delivery, acting on it, then measuring again.
We are building the OS for the highest performing engineering organizations of the future.
Software engineering has always been a balance between the systems teams build on and the people running the process. AI is changing both at once, and the standards that defined good engineering are moving with them.
Gartner projects that AI coding costs per developer will surpass the average developer salary by 2028. The tooling arrived ahead of the evidence about how to use it well.
The practices that shaped healthy engineering teams over the last decade were studied in organizations where a person wrote every line. Some of that evidence still applies. Some of it has not been retested since agents entered the workflow.
Speeding up one stage moves the constraint to the next one. Faster code generation can push the delay downstream into review and testing, so cycle time holds steady or lengthens while developers report working faster. End-to-end value can stall while individual output climbs.
Better meaning software that ships faster, budget that goes further, and a product customers experience as improved. Most available data is usage: seats, tokens, acceptance rates. Adoption has moved past what usage can describe, and the next set of decisions needs impact and value.
How Quotient works
Quotient builds the loop that moves an engineering organization toward its most performant version.
Quotient aggregates signals from the tools and actors across the SDLC to understand how work moves and identify where bottlenecks are forming.
Quotient ranks findings by what is holding delivery back, so a team spends its quarter where the leverage is.
Each finding comes with a research-backed action to take, at both the organization and the team level.
Quotient keeps measuring after the change: impact on speed, quality, developer experience, and overall ROI.
Frameworks
Quotient works closely with the research community to develop new standards for understanding software development and driving the best outcomes.
A framework that translates AI investment into a dollar figure grounded in an organization's own engineering data, with every assumption and constraint documented.
Five stages, from ad hoc experimentation to end-to-end autonomy, assessed across six capability areas. Built from academic studies and work with productivity researchers.
What we value
In a time of radical change, our work is grounded in three unchanging values.
Best practice in engineering is changing quickly, and much of the advice on the market is untested. Our product is built on peer-validated, tested research that correlates to stronger outcomes.
Every recommendation in our library traces back to a published study, named with the method and the sample it came from.
Commits, tickets, tokens, and keystrokes measure activity. Goodhart's law and the streetlight effect describe why activity metrics become incorrect targets.
We look for the highest-signal metrics across systems that translate into measures of impact in engineering.
Software development is a complex system of humans and agents working on top of systems to deliver outcomes. Quotient's methodology focuses on how a team of humans and agents can deliver the highest quality software to their customers.
Research advisors
Principal Applied Research Science Manager, leading the CHISE Lab on human factors in software engineering and how to keep people centered as AI changes the work. She is a co-author of the SPACE framework for developer productivity.
Learning scientist and founder, with a PhD in psychology from UC San Diego, and author of The Psychology of Software Teams. She previously founded and led Pluralsight's Developer Success Lab, where her team studied more than 10,000 developers.
Assistant Professor of Management, with a PhD in Organizational Behavior from Wharton. His research on leadership and team psychological safety has been published in top outlets including Organization Science and Harvard Business Review.
Who we are
Quotient was founded in 2022 and is based in San Francisco. We are built for organizations that want to drive the best outcomes for AI-driven development.
Connect Quotient to the systems your teams already use and get an immediate view of where you stand, what is slowing delivery, and which actions the research supports.