SmartBear Innovation & Technology Culture

How do your teams stay ahead of emerging technologies or frameworks?

At SmartBear, staying ahead of new technologies starts with the people we hire and the culture we build. We look for employees who are curious, motivated and eager to make an impact. Those qualities are reinforced in a quarterly awards program where we celebrate people who demonstrate openness and curiosity. This sends a clear message that learning, asking questions and trying new things are part of the job, not something extra.

Open source is another big way for us to stay close to what’s coming next. SmartBear has a long history of contributing to projects like OpenAPI and we currently support OS projects including Swagger, Pact, SoapUI and Stoplight. The BugSnag team contributes to projects such as KSCrash, which helps us improve the tools we rely on ourselves. Working in the open keeps our teams connected to real developer needs, emerging standards and how technologies are actually used in practice.

Can you share a recent example of an innovative project or tech adoption?

A recent example is our early work with the model context protocol. As this AI-focused standard gained traction, we added MCP generation to Swagger and released the SmartBear MCP server across several products. This enabled teams to experiment with AI-driven workflows early without waiting for the ecosystem to mature.

We also hosted an MCP hackathon, uniting teams from our global offices. We worked with GitHub Copilot, Claude, Gemini and ChatGPT combined with SmartBear MCP server to quickly solve real customer problems and turn disconnected workflows into autonomous, intelligent systems.

One standout project was an MCP server tool that automatically detects and resolves discrepancies between live API implementations, documentation and contracts. The team built an MCP-powered workflow capable of automating the entire reconciliation process with a single prompt. Another standout was a QA intelligence assistant designed to unify data from across platforms and tools into a single, actionable view of product quality. The assistant created a single, trusted view of risk and quality — showing what’s being built, tracking what’s tested and revealing what fails in production.

How does your culture support experimentation and learning?

Experimentation and learning are built into how we work at SmartBear. We value curiosity and initiative and it’s visible through hiring, leadership support and recognition. We remove barriers by giving teams flexibility, budgets for experimentation and regular hackathons, making it easier to try new tools and approaches. In 2025, this was strengthened with a greater focus on AI, encouraging the company to get hands-on with new tools and workflows.

Experiments at SmartBear can start small. One employee started a side project that grew into an internal AI assistant. Available in Slack, BrainBear mines our internal wiki and helps employees find information about policies and projects. After proving its value, it gained the support of CEO Dan Faulkner, was shared companywide and saw rapid adoption. It’s a great example of how ideas are encouraged to grow when they solve real problems.

Most importantly, learning is expected to lead to real outcomes. When experiments show value, we invest in and scale them. Whether it’s MCP support across our products or internal tools like BrainBear, the message is consistent: try things, learn quickly and if it works, we take it further.