Most life sciences data still lives the way it was archived — siloed by trial, by modality, by system — with a data science team standing between every question and its answer. AI doesn’t fix that on its own. It just asks more questions, faster, of data that isn’t ready to answer them.
Tag.bio is the layer that gets your data ready: harmonized, versioned, FAIR data products that sit on top of your existing infrastructure and become the ground truth both people and AI can rely on.
How it works
Point AI at raw, disparate sources — genomics/omics, imaging, clinical trial data, chemistry and assay data — and let it assemble harmonized, versioned data products. The tedious data engineering work, automated.
Ask a question in plain language. The AI invokes a proven protocol for a standard analysis, or runs an ad hoc query directly against the data product — no ticket, no queue.
Protocols execute inside the data product itself, not against your AI budget — so heavy analyses don’t burn model compute, and sensitive data never has to move to get analyzed.
Every AI interaction with a data product is logged — who asked, what ran, what came back — a full audit trail tied to that user’s history, ready for reproducibility and review.
Built for the people doing the science
This isn’t a dashboard you learn or a data team you wait on. Each clinical trial, cohort, or experiment becomes its own data product — and you can ask it questions, in your own words, whether you’re a bench scientist, a translational researcher, or a biostatistician trying to close out an analysis before end of day.
Run a proven protocol when you need a standard analysis done right. Go ad hoc when you’re exploring. Either way, you’re working with governed data — not a spreadsheet someone exported three weeks ago.
See it in action
At a pharma running dozens of active studies, each clinical trial becomes its own standalone data product — cleanly modeled, versioned, and ready to query. Researchers can work within a single trial, or ask a question that reaches across every trial that data product layer touches, using Tag.bio’s frontend or an AI chat interface. The complexity of the underlying data infrastructure stays out of the way.
Governed, quietly
Every data product sits on top of your existing infrastructure as its single governed source — the ground truth for any analysis, human or AI. Every AI-driven query against it is logged and traceable, so as your teams move faster with AI, your audit trail moves with them.
We run a public MCP server at demo.tag.bio — connect to it with a free Claude connector (limited usage) and talk to a real data product yourself. No sales call required to see what this looks like.
