Right Bionic

The Problem

Small biotech companies generate large volumes of expensive data. You look at a powerpoint deck with figures; do you know where the data came from? Can you reuse it? Can you train AI on it?

We've seen a Cambrian-level explosion of data tools in the past two decades, but we can't naively apply them across domains: bench science doesn't naturally map into rows and columns of a warehouse. Sequencing runs, isolate libraries, animal studies, assay results: all of it is heterogenous, manual, and context-dependent. Many of these data problems have been solved: how do we find the right tools, the right plays, and apply them to life sciences?

The companies that figure this out early see real value from their data. The ones that don't end up with fragmented systems, lost institutional knowledge, and scientists who can't find last month's results.

What We Do

Right Bionic helps small to midsize biotech teams build data infrastructure that actually works for scientists.

This means:

• Data architecture that fits the science. We design systems around how your scientists work. Sample tracking, assay data, sequencing results—we make it accessible and usable.
• Infrastructure that scales with you. Cloud computing, data warehouses, pipelines—built with zero-trust security and cost management in mind. Your computational teams get the performance they need without breaking the budget.
• Integration across research domains. Chemistry, biology, computational modeling—different teams speak different languages. We model data entry systems so they can actually collaborate.
• AI implementation for R&D teams. LLMs are useful for literature review and data analysis, not just emails and meeting notes. We'll show your scientists how to use them effectively.

Why This Matters

Who are the audiences for your data?

• Scientific and program leadership need reliable insights, usually ASAP, to make sound decisions
• Machine learning teams need to integrate and version data sets across scientific teams
• Investors and regulatory authorities expect high-integrity, traceable results
• Your IT teams need assurances that digital systems have the appropriate controls and security baked in

Who We Are

Right Bionic was founded by Ryan Bellmore, a data and informatics professional based in the Boston area. A former molecular biologist, Ryan knows how scientists work and prides himself on his passion for working with them. He has over 12 years experience helping scientific organizations–from academic labs, to big pharma, to biotech startups–deal with their data in a practical manner.

Track record:

• Led informatics and IT at Alltrna Therapeutics (40-person R&D team, three office buildouts, multi-million dollar IT budget)
• Built data engineering function at Finch Therapeutics (80k+ sample isolate library, 80TB+ data warehouse, 40% reduction in compute costs)
• Managed scientific data for 600 scientists at Pfizer's Cambridge site

The work includes: ELN training & templates, containerized data pipelines, cloud infrastructure architecture, warehouse design, sample tracking systems, and training scientists on data management practices.

Background: B.S. Human Science from Georgetown (neuroscience research), publications in Neuron and FASEB Journal, former head coxswain for MIT Rowing Club.

Who This Is For

Right Bionic works best with small to midsize biotech companies (10-100 people) that are:

• Pre-clinical or early clinical stage
• Generating data faster than they can organize it
• Hiring computational teams that need infrastructure
• Preparing for regulatory milestones that require data integrity
• Realizing their current system isn't going to scale

If you're still using shared drives and email to manage your data, we should talk. If you're trying to build ML models on that data, we definitely should talk.

How We Work

Projects typically fall into three categories:

Infrastructure buildout — Setting up cloud computing, data warehouses, sample tracking systems. This is architecture and implementation work. We can either maintain the infrastructure ourselves or train a junior engineer to do it.

Data strategy consulting — Partnering with leadership to establish data capture practices, integration plans, and integrity standards. Having "what should we build and why" conversation.

Team training — Teaching scientists and data teams best practices in data management, version control, pipeline development, and AI tools.

Most engagements start with a consultation to understand your science, your data, and where you're trying to go. From there, we propose either a fixed-scope project or ongoing retainer work.

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