Systems store everything and understand almost nothing.
Twenty years of integration moved data between computers, but not what it meant or where it came from.
Evidence infrastructure for healthcare & life sciences
Trust is never the input.
Evidence is.
ScientixAI links every regulated decision to its sources, the systems it passed through and the people who signed off, inside your own environment. When an auditor asks why, the answer is already connected instead of rebuilt by hand. We're choosing a small number of design partners to shape the first release.
The problem
When the auditor asks why, the answer is scattered across a dozen systems.
In science, people are the API.
Application Programming Interfaces are supposed to let computers exchange data. In science, the go-between is a person: a scientist, a monitor or a quality lead reconstructing the past by hand, across systems that do not talk to each other. It is the most expensive API any company runs, and nobody budgets for it.
A question every trial eventually asks
What did we know about Participant 204 when Protocol v3.0 was in effect at Site 123?
The answer is spread across dozens of systems, each with its own login, its own format and its own version of the truth. Today a person stitches it together, weeks later, and the regulator has to take their word for it.
The records a single decision touches today.
- protocols
- site records
- training logs
- consent forms
- data collection forms
- delegation logs
- professional credentials
- lab results
The same gap runs through every regulated decision in healthcare and life sciences, and every one of them pays for it. Many sites on current eSource still open every field by hand before a monitoring visit, and a better screen won't fix that.
Why now
Agents are being deployed on top of the gap. They are guessing across it.
8.3% correct from the schema alone. 78.3% with business context.
GSK measured how often an LLM wrote correct queries against a pharmacovigilance database. Context, not the model, made the difference.
Painter et al., JAMIA Open, 2025. doi:10.1093/jamiaopen/ooaf003 (opens in a new tab)AI is entering regulated decisions faster than the record can defend them.
FDA and EMA's joint principles for AI in drug development call for oversight matched to the risk, and a clear record of where the data came from. Our own comment to FDA: a fast signal is only review-ready if its history travels with it.
Jan 2026 FDA and EMA joint principles. (opens in a new tab)
Every agent now needs a record that can defend its answer.
What we build
Evidence infrastructure for defensible AI
A service your AI and applications call, not another platform to log into. It links the evidence where it already lives, answers only with what the asker is allowed to see, and refuses rather than guess.
Where it came fromThe source system and record behind every fact.
Which rule governed itThe protocol, consent and training in effect at the time, not today.
Where AI actedEvery step an agent took, recorded as it happened.
Who oversaw itThe person accountable for each decision.
How we build
The ideas are open, so the whole industry can benefit.
We're placing three big bets on how healthcare and life sciences should work, and we're publishing the ideas so the industry can test them and build with us. What we sell is how we put them to work.
What we are looking for
Become AI‑native on evidence you can defend.
We're looking for a small number of design partners to shape the first release. We start at the strategy level: where AI should change how your business decides, and what evidence has to be in place before it does. Partners set the order of the roadmap and get evidence infrastructure inside their own boundary while it's being built.