Metropolis Technologies Innovation & Technology Culture

What types of products or services does your engineering team build? What problem are you solving for customers?

At Metropolis, we’re an AI company for the real world. Our AI-powered recognition platform and effortless payment system have transformed one of the most analog industries — parking. We replaced paper tickets and cash with a seamless drive-in, drive-out experience. Now we’re expanding to new real-world interactions like refueling, drive-thrus, retail and stadiums.

Our engineering teams focus on building and scaling technology across 4,200-plus sites serving more than 50 million customers. Key priorities include advancing our proprietary vision systems — Orion and BigMac — creating personalized, frictionless payment experiences, ensuring reliability and security at scale and supporting partners across parking lots, airports and cities through robust data integration and analytics.

Recently, we’ve turned our AI focus inward to tackle a real-world challenge: building Metropolis itself. This led to our newest initiative — context engineering — a service designed to manage AI systems by giving them the right information and tools at the right time.

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

When we introduced AI tooling our Visit Enablement team took a fresh project off the roadmap to improve our map search capability with clustering capabilities. Instead of many labels on a zoomed out map we would show a cluster with the number of sites. Our normal estimate for a project of this size with frontend and backend work was two to three weeks.

An “All AI” approach using tools like Google Gemini, Github Copilot and Claude Code allowed the team to deliver a working prototype in less than a week. They were able to quickly iterate on the product requirements, the visual design and the frontend code/logic using MCP  tools. The backend team also developed better search capabilities and used a common API to connect the systems.

What would that project have looked like if you didn't have AI as a tool to use? 

Without our new AI capabilities, the map clustering project would have faced multiple transitions and delays from team handoffs. A project of this nature would also have required significant time from our busy frontend team. We’ve taken a holistic approach to AI tooling — starting with a tinkering phase that yielded early gains in code completion and reviews. Once agentic AI became widely available in May, we went all-in, using systems thinking to automate entire problems instead of isolated tasks. Our AI transformation came in three areas: developer acceleration — faster, higher-quality code, tests and documentation. Parallel execution — developers can offload security reviews, flaky test fixes and meeting agendas to AI. Process elimination — with MCP services we go from visual design to frontend code and with spec-driven development from idea to implementation, reducing handoffs and delays. We’re now building an “AI Developer” program, managing evolving tools, tracking ROI and investing in context engineering to make AI and humans more efficient. Despite added training, productivity gains are significant, creating a virtuous cycle that aligns with our mission to make the real world.

Metropolis’s approach to innovation is rooted in building machine learning systems that improve continuously in the real world. Rather than treating deployment as the finish line, the company uses a mature MLOps lifecycle that keeps its Recognition Platform learning from changing conditions, new data, and field performance across parking, QSRs, hospitality, and beyond.

“Our approach is built around a mature MLOps lifecycle. Rather than a linear release cycle, we operate in continuous loops: data collection and labeling, model training and experimentation, validation, deployment and monitoring — and then back to data collection when the world changes.”

Metropolis’s engineering culture emphasizes innovation through close collaboration between product and engineering. Engineers are encouraged to approach technical work through a customer lens, contribute during roadmap design, and take ownership of both new features and the systems that keep the platform running at scale.

“Our strong product-driven culture where engineers view technical initiatives through a customer lens while also weighing in on the product roadmap during the design phase. This creates collaborative ownership over both new features and long-running systems critical to the platform.”