GxP Compliance Studio runs its validation document pipeline on local NVIDIA GPUs, so regulated life sciences teams get audit-ready AI without sending a single file to a shared cloud.
TRACK RECORD
Early, not starting from zero
13+ yrs
Regulatory compliance and validation leadership behind every requirement template
Inception
Admitted to the NVIDIA Inception program on the strength of the platform
RISE Grant
Maximum award, Village of Schaumburg’s inaugural Microenterprise cohort
Anchored at the Roosevelt University RISE incubator in Chicago’s life sciences corridor, with an internship-to-hire pipeline drawing from Illinois universities.
APPROACH
The problem with validation documents
Every computerized system a life sciences company puts into production has to be validated first, against rules like 21 CFR Part 11, EU Annex 11, ICH Q9 and GAMP 5. The foundation document, the User Requirements Specification, is still built the way it was twenty years ago: interviews, workshops, and rounds of manual review. The cost isn’t only the consultants’ hours. It’s the delay before a therapy reaches a patient.
01
Interview
A guided conversation gathers requirements directly from the people who know the system, without a workshop calendar.
02
Ground it
Every requirement is checked against the regulations that justify it, so it carries a citation instead of an assumption.
03
Structure it
Requirements become records with a criticality and a source, reviewable line by line instead of buried in prose.
04
Render it
The final document is produced deterministically. The same input hands back the same document, which is what an audit actually requires.
Built on NVIDIA
Local inference isn’t a nice-to-have here. It’s the reason a Head of Quality can say yes to AI at all, because the data never has to leave a customer’s firewall.
Now
Production workstation
Every pilot today runs on a GeForce RTX 4090, keeping validation data on hardware we control end to end.
Next
Expanding local capacity
We’re evaluating the NVIDIA DGX Spark desktop as our models grow, without giving up the local-only guarantee our customers require.
Ahead
Scaling beyond one desk
As pilots turn into deployments, we’re exploring server and cloud-hosted GPU options that carry the same local-inference model further.
Exploring
Applied research
We’re following NVIDIA’s LIDAR work in hospital settings and its use in EV-oriented smart vehicles, which lines up with a separate green energy and fleet initiative we’re pursuing.
Our customers cannot send validation data to a shared cloud endpoint. That single constraint is why NVIDIA hardware sits at the center of our architecture rather than at the edge of it.
AFFILIATIONS
Program affiliations
NVIDIA INCEPTION PROGRAM
NEXT STEPS
Two conversations we’re ready to have
If you run quality or regulatory ops
See whether a pilot fits the validation backlog you already have.
Request a pilot
If you’re with NVIDIA or another partner
Talk through compute, a pilot introduction, or co-marketing support.
Talk with our team