Modern biology runs on precision, yet so much of the daily lab workflow remains stubbornly manual. Even today, one of the most fundamental steps in any experiment—counting cells—depends on hand-held counters, variable techniques, and operator judgment. For research labs, hospitals, and bioprocessing facilities, this slow and inconsistent process drives up staffing costs, introduces errors, and breaks experimental reproducibility. That bottleneck has persisted for decades, not because it is unsolvable, but because the tools that exist have never been designed for where cell counting actually happens: inside the biosafety cabinet.
That is the gap Qiushi Wang, founder and CEO of Labro Inc., set out to eliminate. A computer-vision engineer with a systems mindset, Wang is the architect behind a new category of automation device—a compact, wall-mountable imaging module that lives directly inside the biosafety cabinet and counts cells autonomously using convolutional neural networks. His work is already attracting recognition, including first place at the NYBPC Capital Region competition in 2024, a signal that a disruptive shift in lab workflows may finally be within reach.
“Cell counting should not be a task that consumes hours of a trained scientist’s week,” Wang says. “If AI can drive cars, it can certainly count cells. The challenge is building a system so intuitive and reliable that it disappears into the workflow.”
His vision began taking shape in late 2024 with the launch of Labro’s flagship project.
Rethinking a 70-Year-Old Workflow from the Ground Up
The problem, as Wang describes it, is not simply inefficiency. Manual counting introduces variability between technicians, inflates training costs, and slows down high-throughput labs that need results in minutes, not hours. Existing automated counters are often too large, too incompatible with sterile workflows, or too expensive for general-use settings. The result is a workflow trapped between outdated tools and modern expectations.
Wang’s solution is elegant in its simplicity: a cabinet-ready optical module that mounts on the interior wall of any biosafety cabinet. It captures images of cell suspensions and runs them through a CNN trained on annotated datasets to produce instant, hands-free counts. The device requires no pipetting onto external machines, no manual adjustments, and no exposure of samples to unsterile environments.
But designing a product that works inside a biosafety cabinet required rethinking constraints that traditional manufacturers avoid. Hardware must minimize airflow disruption. Optical components must withstand repeated sterilization. The interface must be operable from inside the cabinet, where gloves reduce dexterity. These considerations shaped every design decision.
“The hardest part wasn’t the CNN,” Wang explains. “It was designing a form factor that respects the biology. The cabinet is sacred space—any device that enters it has to behave like it belongs there.”
Wang led all aspects of the engineering effort, from electronics and optics to embedded software and UX design. He also served as the primary lead for customer discovery through the NSF I-Corps process, immersing himself in the lived realities of academic, clinical, and industry labs. Those interviews informed both the product roadmap and the go-to-market strategy, helping Labro secure selection to the NSF I-Corps National program in 2025.
Turning AI Into Practical, Everyday Lab Infrastructure
The core of the system is a deep-learning pipeline trained to identify and enumerate cells across multiple morphologies, densities, and imaging conditions. Real-world cell cultures are messy—debris, clusters, irregular shapes—and Wang’s models needed to generalize across all of it. Training required crafting high-quality annotated datasets, validating detection under different lighting and sample conditions, and building inference pipelines optimized for a compact embedded device.
“Biology doesn’t happen in ideal conditions,” Wang says. “If your model only works on a perfect sample, it’s useless in the real world. We designed for chaos, not perfection.”
Once deployed, the device eliminates the majority of human error and reduces counting time to seconds. For a typical lab, the staffing savings alone are estimated at roughly $8,000 per user per year, not including gains in reproducibility and reduced training time. Across the broader market—which analysts value at approximately $2 billion—the potential for cost savings and workflow modernization is significant.
The product’s architecture also accommodates multi-user environments, data logging, and integration with LIMS systems, making it suitable for both research labs and regulated workflows. Its compatibility with biosafety cabinets unlocks a category of automation that previously did not exist.
Bridging Engineering and Biology Through Purpose-Built Design
One of Wang’s strengths is his ability to treat biology not as a problem to be automated, but as an environment to be respected. That philosophy earned Labro recognition on global innovation stages, including second place at the 2025 “Win in Suzhou” International Competition and the Most Innovative Award at the 2025 NextGen Entrepreneur Summit.
“When you build hardware for scientists, you are not just engineering a product—you are engineering trust,” he reflects. “A biologist should never have to wonder whether a machine is compromising their sterility or their data. The device has to feel like a natural part of the bench.”
That trust is emerging not only through design, but through validation in upcoming pilots across academic and clinical laboratories. The team is currently preparing for expanded field testing as it progresses into mid-2025, with early users already reporting dramatic reductions in repetitive workload.
The Future of AI-Native Lab Tools
Wang sees this cell-counting device not as an endpoint, but as the first module in a larger shift toward “cabinet-native automation”—tools that remove manual variability by embedding intelligence directly into existing sterile workspaces.
“Every time a scientist touches a pipette to perform a repetitive task, that’s a signal that automation has not gone far enough,” he says. “We want to take those tasks off their plate so they can focus on actual science.”
His engineering discipline, customer-driven product thinking, and entrepreneurial momentum suggest he is well on his way. With multiple innovation awards already behind him—including first place at the 2024 NYBPC Capital Region competition—and growing demand from pilot labs, Wang’s work is poised to change how teams around the world conduct routine cell biology.
If the future of the life sciences depends on reproducibility, speed, and intelligent automation, Qiushi Wang’s cabinet-ready AI cell counter may well be the device that brings that future into everyday practice.
