Money Forward brings Cursor’s coding agents to product, design, and QA
Money Forward set out to bring coding agents to every team that touches how software is built. It started with engineering, where Cursor quickly started saving developers 15–20 hours a week, then expanded to product, design, and quality assurance (QA).
Today, over 1,000 employees at Money Forward use Cursor daily. QA engineers are generating test cases 70% faster. Product managers are analyzing production code to write better specifications. Designers are prototyping directly against live frontends and analyzing user data to refine designs.
Proving value in engineering first
Money Forward’s engineering team was initially using other external vendors for code autocompletion and basic AI chat functionality. Adoption had largely stalled as developers were not seeing meaningful time savings on software tasks.
After introducing Cursor, the number of engineers using coding agents increased by 30% within just a week.
We held an engineering all-hands where we showed that Cursor’s agents could actually tackle entire software engineering tasks from end-to-end. The bottoms-up demand from developers was immediate.
Developers are now individually saving an estimated 15–20 hours a week with Cursor across tasks like:
- Refactoring service layers for Money Forward’s iOS applications
- Optimizing Rails applications to drive 10x performance improvements
- Managing AWS and GCP deployments with Terraform
- Migrating legacy front-end services from Vue to React
But as engineering began shipping software faster, product, design, and QA became the constraint.
Evaluating Cursor for a company-wide rollout
Money Forward’s Engineering Productivity and AI Research (MEPAR) department evaluated several different AI coding tools before selecting Cursor for its company-wide agent rollout.
Cursor’s model-agnostic infrastructure lets us parallelize long-running tasks across asynchronous cloud agents. The agents connect to our internal tools for fast context retrieval without the limitations of local hardware. Cursor's role is expanding quickly across all our teams.
A few advantages made the difference:
- Minimal setup: Users can start building with agents immediately, without any complex environment configuration. This made adoption practical across functions with varying technical depth.
- Visual capabilities: Cursor’s built-in browser made it easy for designers and QA engineers to visually verify agent changes. These teams preferred Cursor’s rich interface over terminal-based alternatives, where reviewing visual output required extra tooling.
- Unified agent workspace: Cursor offered a single platform for code generation, review, testing, and debugging so users didn’t have to switch between tools to do their work.
- Large codebase performance: Money Forward maintains complex, interconnected production systems. Cursor’s context retrieval performed reliably against these codebases, which was critical for non-engineering teams interacting with production code for the first time.
Cursor has spread beyond engineering to design, product, and QA. These groups had low acceptance rates for other tools that haven't invested in a robust UI and user experience.
QA automates test generation and moves upstream
Before Cursor, QA engineers were manually reading product specs, developing test cases for each user story, and writing test scripts.
Now, QA engineers feed Cursor relevant Jira tickets and Notion docs using MCPs. One agent then generates structured test cases while a second agent translates them into Playwright scripts.
As a result, time spent on test generation has decreased by 70%. QA teams are now spending more time influencing product quality earlier in the software lifecycle by focusing on risk-based testing and quality gates.
The QA team now uses Cursor to analyze incidents, automate test results, and review specs before development. Cursor is changing how we ensure software excellence.
Product uses Cursor to refine requirements
Cursor helps PMs extract system relationships from repositories, generate architecture diagrams, and draft PRDs that are grounded in real implementation details.
This approach has helped product teams identify edge cases and overlooked constraints before engineering work begins, improving the overall efficiency of the software development lifecycle.
Even when specifications don’t exist in our docs, Cursor can identify them directly from the code. This allows us to develop better product requirements for engineering to build from.
Design works directly against production code
Historically, designers worked from static mockups and secondhand descriptions of system behavior. Designers were often removed from the actual user journey and business data that determined whether a feature succeeded or failed.
Designers at Money Forward now use Cursor’s browser capabilities and fullstack context to iterate against application frontends directly in code. Designers also use Cursor’s agent and MCPs to directly access product analytics and refine designs accordingly.
With Cursor, I can access product specs and data myself. That allows me to design with a clearer understanding of how the product actually behaves, not just how it's described.
If you are interested in bringing agents to every team that touches your SDLC, reach out to start a Cursor trial.