AI Engineering for Life Sciences
You do not have an AI problem. You have a foundation problem.
The models are rarely the issue. The foundation beneath them is. That foundation is what we engineer.
There are more AI projects than ever. Almost none of them are paying off.
95%
of enterprise AI pilots produce no measurable business impact.
MIT
80%
of data governance programs will fail by 2027.
GARTNER
68%
of pharma leaders name weak data foundations as the top reason AI fails.
ZS, 2025
Ask the people running them why, and they point to the same place. The foundation.
This is not an AI problem. It never was.
We build the layer that makes AI work, and we build it to be yours.
Three things have to be true before AI earns its keep.
The data has to be governed and structured.
The meaning of that data has to travel with it.
And the intelligence has to reach the people making decisions, in the flow of their work.
We engineer all three. On the stack you already run. Then we hand you the keys.
Give AI a raw database and it guesses. Give it engineered context and it knows.
In a published study, a model answering business questions from a raw schema was right 8 percent of the time.
With engineered context, 78 percent.
That gap is not a tuning problem. it is the entire job.
See how we build foundations →
Foundations you own. Not platforms you rent.
The market sells you a platform you license and never escape, or a team you pay forever. We sell neither.
Stack neutral.
We build on Databricks, Snowflake, Microsoft, and AWS. The environment you already run, made AI native. no migration to a SENTIER platform, no lock in, because there is no SENTIER platform to sell.
Accelerated.
A library of pharma foundations, models, and governance patterns built since 2017. We do not start from a blank page, so neither do you. The build is a focused engagement, not a multi year program.
Built to own.
We engineer the capability into your team and leave. Dependence is the competition;s business model. it is not ours.
Engineered by depth.
Pharma commercial data punishes generalists. Our people sit where pharma, technology, data engineering, and data science meet.
All of it without the overhead of traditional platforms or consultants.
What the foundation powers is decision velocity.
Decision velocity is the time between a question and an answer you can act on with confidence. most of that time is lost in the foundation, not the model. Engineer the foundation and the gap collapses.
Data Foundation.
Governed and structured, so no one relitigates the numbers. Decisions start from agreement, not argument.
Context.
Meaning travels with the data, so the question is understood the first time, not after three rounds of clarification.
ML intelligence.
The answer carries its expected impact, a predicted outcome, not just a number to interpret.
Agentic delivery.
The answer reaches the people deciding in the flow of their work, the moment asked, not weeks later out of a queue.
The companies that build with us.
