Ro (Ro.co) Innovation & Technology Culture

Tell us about a recent product your team launched. How does it drive Ro’s mission forward?

A big part of the culture at Ro is keeping our eyes and ears open to patients’ needs and the gaps in their healthcare. Launching Ro Sparks was a huge step in patient-centric innovation at Ro, maximizing the ease and efficacy of erectile dysfunction treatment.

We heard what patients were saying about the shortcomings of traditional ED meds, such as how it “kills the mood” to have to plan ahead and take a pill so far in advance. So we launched Ro Sparks, which are designed to dissolve under your tongue where the active ingredients absorb directly into your bloodstream for faster results, lasting up to 36 hours. At the end of the day, organizations have to remember the people behind the numbers and the important role we play in the lives of those who choose us, trust us, bring us their problems and ask us for help.

What obstacles and challenges did your team encounter — and overcome — while launching Ro Sparks?

There’s no innovation playbook for a full-stack telemedicine company. From developing new products to launching them on our integrated platform, we have to take into account — and optimize for — the specific needs and experiences of providers, pharmacists and, most importantly, patients. Product launches are a herculean effort even under the simplest of circumstances, but we’re also a hybrid team that’s spread all over the country. In the end, we just kept soft knees and a positive mental attitude while leaning hard on the three “Cs”: communication, collaboration and camaraderie.
 

How has the launch of Ro Sparks changed the way your team operates?

Each week brought new challenges, but we crossed every bridge and jumped every hurdle as a team. It wasn’t always graceful, but we always made it over. Critically, to increase the ROI of our launch project efforts, we turned every hard-earned problem resolution into a newly unlocked capability that we can operationalize and leverage again in the future. We also developed a new level of rigor and commitment to ruthless prioritization of day-to-day tasks and timeboxing of major decisions. Ultimately, the success of the Ro Sparks launch, and any launch at Ro, is fueled by the team’s unwavering grit and dedication to doing right by our patients.

What project are you most excited to work on in 2025? 

Our team is working on an exciting LLM-based pipeline to triage the millions of incoming patient messages we receive each year. The goal is to help our incredible team of nurses, providers and patient advocates by intelligently routing messages to the person most qualified to help with that specific question or need. This will give our care teams more leverage and save them time while supporting our patients. This is compelling because it directly solves an immediate, real-world problem.

What does the roadmap for this project look like? 

Our goal is to streamline response times for patients while reducing manual triage time for our care teams. We’ll start with classifying and routing patient messages to the correct team of responders. From there, we will develop a series of LLM prompts designed to identify situations and route them with high accuracy to specialized teams. We’ll also focus on using LLM to enable context gathering, to pull in relevant details to summarize the issue concisely for the responder. This will reduce time spent searching for information and allow for quicker, more informed decision-making. 

To bring this vision to life, we will collaborate with multiple teams across the organization — from back-end and front-end engineers to designers to operations specialists to data analysts. This highly cross functional group will play key roles to ensure this new capability provides a seamless user experience. We will continuously review patient messages with human experts to refine the model’s accuracy. Coordinating these audits is a critical part of the feedback loop that will inform our iterative improvements to deliver a better experience for both patients and our care team members.

What in your past projects, education or work history best prepares you to tackle this project? What do you hope to learn from this work to apply in the future?

Over the past decade, I’ve worked on a wide range of machine learning projects, spanning natural language processing and computer vision. However, my deepest focus has always been on NLP — originally working with small language models and continuously pushing the limits of what was possible.

I identify strongly as a prompt engineer, having engaged in some form of prompt engineering as early as 2016, long before the term was widely recognized. My experience working with early deep learning architectures gave me a strong foundation in understanding model behavior, and an intuition for crafting the inputs and harnessing the outputs of models to coax out new product possibilities. These skills have only become more relevant and valuable today with the rise of powerful transformer-based architectures.

Through this project, I hope to refine my ability to bridge the gap between AI capabilities and real-world operational needs. Ultimately, this work will shape how I approach AI-human collaboration, not just in healthcare but in any domain where AI needs to function as an assistant rather than a replacement.

What’s your rule for releasing fast — and what KPI proves it works?

Make the safe path the default path. Complexity creates drag, risk and cognitive load (for humans and agents). At Ro, we invest in paved-road tooling like Rodeo, our service framework, which bakes in primitives for observability, authentication, compliance, redundancy and scaling. The KPI is not just deployment throughput, rather, it is throughput cross-referenced with incidents and defects. Shipping faster only matters if we are not creating more cleanup work or breaking things for our patients in the process.

Which standard or metric defines “quality” in your stack?

Quality means low-friction systems where engineers can move fast without working around the system — the safe path is paved, observable and predictable by default. That translates into a better patient experience because teams catch issues earlier, recover faster and reduce the chance of defects reaching patients. Things will break, so we try to “score the glass” so they break in manageable ways. The standard is to make problems visible, containable and learnable.

Name one recent AI/automation shipped and its impact on the team or business.

In a broad, distributed system not everyone has high context on all parts of it. Ro has developed a Slack-based incident assistant that helps on-call engineers triage issues using Ro-specific context. It correlates deployments, anomalies, service ownership, architecture and product context from our observability and incident systems. The goal is simple: reduce the time engineers spend hunting for signals and help them get to the root cause faster for our engineering stakeholders and to ensure a seamless patient experience.

How do engineers influence innovation and product strategy?

At Ro, engineers are product partners, not just implementers. They actively shape product strategy by identifying architectural challenges, surfacing recurring pain points and uncovering opportunities to build new capabilities.

One example is our work on a unified health data record. Our engineers recognized that patient health data spread across multiple microservices created technical complexity and made it harder to deliver a connected patient experience. Addressing this challenge led to a reusable foundation for health data that can support new capabilities, including clinical interpretation and alerting. What began as an architectural problem became an opportunity to improve the patient experience and unlock new product capabilities.

Engineers also help eliminate operational bottlenecks. By using tools like centralized Claude skills, they’ve automated parts of the workflow for updating structured health records alongside patient-facing online visits, reducing manual coordination between clinical and engineering teams.

This proximity to our products and workflows allows engineers to turn technical insights into scalable solutions that benefit both patients and the business.
 

How do you decide when to adopt new tech or tools?

Before evaluating a new tool, we need to understand what problem we’re trying to solve and why it matters. Our primary lens for adopting new tech is whether it solves that problem and improves the patient experience. Ro also creates opportunities for engineers to experiment with emerging technologies. A recent example was an engineering-wide hackathon centered around Claude. Engineers identified bottlenecks in our workflows, then prototyped ways LLM-enabled tools could address them.

One problem was time spent on repetitive “housekeeping” work, like updating dependencies. This experimentation led to Zero, our coding agent. By taking repetitive work off engineers’ plates, Zero frees them to focus on complex technical challenges and building products that improve patient care. Another area was validation and user acceptance testing. Engineers built a Claude-enabled tool to generate test patient scenarios more efficiently, shortening feedback loops and helping us ship high-quality features faster.

This is how we approach adopting new technology at Ro — start with a real problem, create space to experiment, prove tangible value and invest further when it demonstrates how it can make our teams or products
 

What practices help engineers balance experimentation and reliability?

At Ro, experimentation and reliability aren’t treated as opposing goals. We design our processes so teams can test ideas quickly without compromising the quality of the patient experience.

A crucial part of this is making incremental changes and adding telemetry. For example, we may release feature-flagged experiments aimed at improving the patient journey, such as streamlining the insurance approval process. These experiments include telemetry at the data, application and UX layers, allowing us to understand whether an idea works and how it impacts patients. We use that insight to iterate before broader adoption.

We apply the same philosophy to our software development workflows. When introducing tools like code review agents, we don’t immediately roll them out across every engineer and pull requests. We start with a smaller cohort and narrower use cases, observe performance and refine the workflow.

This approach preserves the speed and creativity experimentation requires while maintaining a high bar for reliability. Guardrails like feature flags, staged rollouts and strong observability ensure learning and quality reinforce each other rather than compete.