Developers who understand biotech workflows and pipelines don't all live in the same city. We built a remote-first team so we could hire for domain expertise first and geography second, bringing over 20 years of life sciences software experience to every project.
Unlike consultancies that work across dozens of industries, we've chosen to focus exclusively on biotech. This specialization is our strength.
We understand biotech. From proteomics workflows to LIMS requirements, sample tracking to instrument control. We've built solutions across the entire biotech landscape. Our team has spent years learning what matters in your industry, not just applying generic software patterns.
When a generalist developer encounters an unfamiliar problem, they search for solutions. When our team encounters a biotech challenge, we draw from years of specialized experience. We anticipate problems before they arise because we've seen them before, in similar contexts across different companies.
We're not bolting on security as an afterthought. Encryption, access controls, audit trails, and data validation are woven into how we design systems from day one. Biotech data demands this level of rigor.
We're invested in biotech's success because that's our sole focus. We understand your market challenges, your competitive pressures, and your growth trajectory. We're not chasing the next project in a different industry. We're building long-term relationships with partners we're genuinely passionate about.
We are small on purpose. Our remote setup lets us hire for domain expertise first and geography second, and it means the engineer who scopes your work is the engineer who builds it.
CJ has spent more than twenty years building software in complex scientific domains, across proteomics, mass spectrometry, and genomics. He is a hands-on engineer rather than a manager who stopped writing code, and he is usually the person you talk to first.
Nicola works at the intersection of science, technology, and business strategy. Her scientific background helps the team understand what a client's domain actually demands before any code is written.
Eli builds and hardens the systems that have to keep running. He leaves no stone unturned, whether the work is optimising a slow pipeline stage or making an interface something a scientist can use without a manual.
When your software processes research data that drives discovery, there's no room for sloppy engineering. These principles guide how we work.
Multi-omics datasets, instrument protocols, and stringent security requirements create inherent complexity. Our job is to absorb that complexity so your researchers don't have to. We reduce convoluted pipelines to clear, maintainable systems and strip away unnecessary abstraction until only the essential logic remains.
Research doesn't wait, and neither should your software. We deploy completed features every sprint through automated pipelines, so your scientists get new capabilities often. When an experiment reveals a new requirement, we can respond in weeks rather than waiting for the next quarterly release.
We apply the same rigor to our software that your scientists apply to their research. Pipeline accuracy, processing throughput, data integrity checks, and system reliability are all tracked and measured. When results need to be publishable and reproducible, the infrastructure behind them has to be trustworthy.
If a proposed architecture won't scale with your data volumes, we'll say so. If an off-the-shelf tool solves the problem better than custom code, we'll recommend it even though it means less work for us. Biotech projects are too important for polite evasion. You get our honest technical assessment every time.