Understand your data landscape
We look at how your data, systems and standards fit together, and where it matters most.
LIFE-SCIENCE DATA INFRASTRUCTURE · CONSULTANCY AND ENGINEERING
Scientific data.
Connected and in context.
We help pharma, biotech and biomedical research organizations connect the systems they already have and keep the context of every dataset, so data stays findable, understandable and reusable. Without locking you into another vendor.
Why Cellonautica
Close to the standards
We contribute to the open standards and tools we build on, so improvements can happen at the source.
Your cloud, your data
By default, everything runs in your environment and your data stays there. Any other setup is agreed with you.
No lock-in
Your data stays in open formats with documented interfaces, so you’re never tied to us or to any single vendor.
THE PROBLEM
Science moves faster than the systems that hold its data
Research organizations run many systems from many vendors, each holding part of the picture. The context that makes data meaningful often stays behind.
Data spread across vendors
Instruments, samples, analysis and storage each live in their own system, and none of them sees the whole experiment.
Context gets lost
How data was made, what was done to it and whether it can be trusted rarely travels with it. Finding and reusing it later is slow.
Every change is a project
Connecting two systems or adding a capability means waiting on vendors, paying for integration work and losing months.
HOW WE HELP
Connect what you have. Keep the context.
Consultancy and collaborative engineering, shaped around your science and the systems you already rely on. From making data ready for AI and LLM workflows to advancing the open standards your field depends on.
Connect and extend
We help your systems work together, and add what’s missing.
Build with your team
We engineer alongside your scientists and informatics teams, on open standards.
Advance open standards
We develop specifications, validation tools and roadmaps with the communities and organizations that rely on them.
HOW AN ENGAGEMENT WORKS
From first conversation to working infrastructure
Engagements range from a focused assessment to a year-long pilot with ongoing support. Most follow the same four steps.
Assess
We map your data sources, systems and requirements, and agree on scope, standards and architecture with your team.
Build in milestones
Working software arrives early and grows in agreed milestones, so you see progress and can steer it.
Validate
We test on representative data, measure performance at realistic scale, and refine what doesn’t hold up.
Hand over and support
Documentation and knowledge transfer leave your team in control, with ongoing support during the engagement and beyond if you want it.
OUR APPROACH
Context that travels with your data
Metadata, provenance and quality are captured as the science happens, and mapped to the standards and ontologies you rely on. Scientists keep working in their own terms. The structure happens underneath.
- FAIR by default
- Findable, accessible, interoperable and reusable data as a by-product of everyday work, not a separate clean-up project.
- Your terminology
- Scientists use the words natural to their field. Validation and mapping to ontology concepts happen in the background.
- Vendor-neutral
- We connect the systems you already have, and you stay free to change them. No new lock-in.
- Built for scale
- Cloud-native storage and processing for large, multimodal datasets, with performance measured on representative data.
- Open standards and open source
- We contribute to the open standards and open-source tools we build on, so we fix problems at the source instead of working around them. Knowledge stays with your team when a project ends.
WHERE WE START
Bioimaging, spatial omics and single-cell data
We start where our team’s expertise runs deepest: large, multimodal imaging, spatial and single-cell data, and the metadata that gives it meaning. The same principles apply wherever scientific data lives.
- BIOIMAGING
Microscopy data connected to the samples, protocols and analyses behind it.
- SPATIAL OMICS
Images, molecular measurements and annotations kept together, with their provenance.
- SINGLE-CELL
Single-cell measurements organized with their annotations, so they can be combined with imaging and spatial data.
- AND BEYOND
The same approach extends to other domains as your data landscape grows.
WHY NOW
AI is only as good as the data it can understand
Analysis tools and AI systems need data that is structured, findable and carries enough context to be interpreted correctly. Without that, they guess.
Well-described data, with clear and machine-checkable descriptions of what it contains, lets people and machines work with it reliably. AI then helps with the work itself: generating metadata, searching, mapping terms to ontologies and exploring data.
Preparing data, metadata and context so AI-assisted workflows can use them reliably is part of the work we do today.
WHERE WE’RE HEADING
Infrastructure you can build on
Our own software grows out of this work: a vendor-neutral foundation where context is captured as you work, and where components can be added, replaced or adapted as your science changes. When something is missing, you should be able to build it quickly and connect it to your existing data, without starting a large project.
ABOUT CELLONAUTICA
Close to the standards we work with
Cellonautica is an early-stage company working on life-science data infrastructure. We are core contributors to widely used open-source scientific software and take part in developing the community standards for bioimaging and spatial data. We know these formats from the inside, we can improve them where it matters, and the work we do with you stays aligned with where the field is heading.
GET IN TOUCH
Talk to us about how we can help
Tell us about your data, your systems and what you would like to change. We’ll get back to you to set up a first conversation.
Talk to us about your data