Benchling Bioanalytical
A unified platform to design and execute lab tests at scale
Benchling creates software for life science teams, ranging from academic labs to some of the largest pharmaceutical companies in the world. What started as a simple set of tools had evolved into a full platform, with over 200K scientists and 1,300 companies relying on it.
In 2025, we launched Benchling Bioanalytical, a product to help large enterprise customers develop and execute tests during drug development.
- Role
- Design lead. Co-led discovery with a senior PM, then coordinated design work across 4 designers and 5 engineering teams.
- Timeline
- 2024–2025
- Outcome
- Launched with our charter customer, onboarding 400+ users, reduced throughput time by 90%, and replaced 6 systems with 1.
- Skills
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Breaking the bottleneck
During drug development, companies run countless analytical tests to ensure the quality and efficacy of their products and processes. Developing them is a massive investment, where each test must be experimentally designed and validated. Novel biologics makes this even harder, since cells are super sensitive and easily contaminated.
“The thing that was taking the longest wasn't doing the experiments in lab — it was getting data from all the systems collected and analyzed.” — Roy Helmy, Associate VP, Merck
Science is a team sport
We often think of scientists as the heroes, but there's a huge team of people in the background also doing critical work.
Like many organizations, there's a distinction between the "brain" and the "hands" of the lab. Lead scientists (or "principal investigators") set the overall direction while lab scientists do day-to-day experimentation.
There's also an obsession with getting ever higher throughput in their process. So, they have lab automation people who specialize in working with robots who can process thousands of samples at once. Data scientists who can help design complex experiments and sift through enormous datasets. IT managers who build and maintain systems that orchestrate and manage all this work.
Flexibility to learn, strictness to execute
Again, think of how recipes move from a test kitchen to a chain of restaurants. Early on, scientists need flexibility to tinker with different ideas and just enough documentation to build on what they learned. Eventually, they need to thoroughly document and train other people to execute that recipe reliably, even when they're not around.
This creates enormous friction in the process and no one's really solved it. In software, having flexibility often means loose definitions and unstructured data. How do you turn that into a complex and rigorous step-by-step process that can be checked and monitored for compliance?
Getting out of Zoom meetings and into the lab
Death by a thousands hand-offs
Disconnected tools, manual work
Different tools capture and document data, but they don't talk to each other. Scientists end up manually chasing down samples, checking expiration dates, and recording work they'd done hours earlier. On average, a scientist needed to work across nine systems to access the information they need.
Duplicative data structures
Different materials and instruments live in different systems, each with their own data structure. No one could agree on what a "sample ID" was, so they existed in four different formats, relying on know-how and Python scripts to get everything consistent. Multiply this across different systems and it led to a lot of frustration and confusion.
Processing data and reviewing findings
Robotics made execution faster, but also more data than teams could handle. Given all the manual steps in handling and processing data, the tedious work rapidly piled up. Lead scientists talked about manually reviewing reports, even on nights and weekends.
No window into the end-to-end process
Teams didn't have visibility into each other's work, which led to confusion around timelines and requests. Every team wished the other teams understood their work better. When are my samples arriving? Where did the test results go?
35–50%
of scientists' time spent on non-scientific tasks
9
different systems to complete workflow
3 months
for IT to implement process changes
100%
of interviewees felt it was hard to find data
What guided our work
End-to-end data flow
Data needs to move seamlessly from one step to the next without anyone having to double-check it survived the hand-off. This was the core job we had to get right and it requires us to think rigorously about how our individual features fit into the big picture.
Flexibility where it matters
We need to consistently ask, "What does this look like in early development vs. later when it's locked down?" We need to articulate the difference and establish patterns and language that help scientists distinguish the two.
Consistency and clarity
We should always care about this, but remember the stakes are higher in drug development. We're introducing a lot of new concepts and UIs, and we need to be thoughtful what they mean and how they sync together. Ultimately, consistency builds trust in our system.
Build with leverage
We must think about how to build on shared components and extending before re-inventing the wheel. This means getting out of team siloes and actually looking for points of leverage, whether it's a backend system or a UI component.
Building two products on a single foundation
Throughout discovery we incrementally validated this approach, but also identified gaps and risks. Bioanalytical required first-class support for designing plates (physical grids with tiny wells) that were highly integrated with lab instruments. We also hadn't yet built out a "locked down" version of Benchling yet, which was critical for automated sampling testing later in the project.
Closing these gap turned out to be one of the most complex parts of the build.
Leading the design work
My approach to leading design work was to give people real ownership, and step in deliberately when they needed it, whether that meant pairing on design explorations or troubleshooting team dynamics.
I led a design kickoff to walk through our insights and conceptual flows, then worked to balance that ownership with speed. Sometimes that meant helping a designer sift through explorations to focus on one core idea. Other times, it meant genuinely listening when someone was struggling and helping them find their footing.
Underneath it all, I just wanted to be a steady presence for the team. Things got overwhelming at times, and I tried to be someone people could count on to listen without judgment and keep them moving forward.
Design, experiment and execute testing protocols at scale
Manage testing protocols
Scientists can define protocols flexibly and iterate on them until they're ready to publish.
Design and run experiments
Scientists can flexibly design plates which are used by instruments to automate lab work.
Capture and analyze data
Scientists can execute work by hand or through lab automation, analyzing large volumes of data seamlessly.
Store and track lab materials
Lab managers intake materials and make sure they're safely stored in the right place, with full traceability when they're used in experiments.
Review and track progress
Lead scientists and quality teams can review work, trace back results, and manage project progress.
Shipped, validated, and expanded
After months of work and weeks of rigorous user acceptance testing, we saw Merck go live with a public announcement and 400 users onboarded.
90%
reduction in data throughput time
25%
reduction in manual tasks
3
expansion or new customer opportunities
1.1M samples
supported, with six legacy systems phased out
Leading through trade-offs
Getting Bioanalytical to launch meant a lot of hard trade-offs. We leaned hard on one core principle, fast and reliable data flow over visual polish, and told ourselves we'd circle back to tighten the experience later. In practice, the team shifted straight back to Bioprocess once we launched, so some of that polish never happened.
Building two products in parallel is a hard juggling act, and we didn't always get the balance right. But pulling designers together every week to walk through our end-to-end flows broke down silos that had built up, even within our own design team. People walked away with a much deeper understanding of how their work connected to everyone else's, leading to more collaboration happening spontaneously, not just in the meetings I'd scheduled.