Right Bionic, Bio-Data Consulting

Right Bionic is an independent consulting firm, specializing in data management and computation for life science teams. We help you own your data lifecycle from data ingestion to analysis and reporting. Too many data tools are out of step with the needs of scientists and decision-makers... but not anymore.

We come with experience in large and small drug development teams and specialize in the following technologies:

• Benchling ELN and registry configuration
• Teaching DevOps and coding to scientists
• Pipeline-ing NGS workflows
• Data warehouse design and ETL ingestion
• AI Education for Scientists

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

Check out our blog for case studies.

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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