Example scenario — not a named customer
A formulation team finds what it already knows
A pharmaceutical company with in-house formulation and quality teams
A pharmaceutical company connects its studies, lab records and batch documentation in a searchable AI system that runs entirely on its own infrastructure.
- Enterprise AI
- server tier
- 2× H100
- GPUs on site, or similar
- 0
- research files leaving the site
The problem
Years of studies, lab records and batch documentation were stored across separate systems. Researchers repeated experiments because earlier results — especially unsuccessful ones — were hard to find, and preparing regulatory material meant searching the same sources again and again.
Constraints
Formulations and research data are core trade secrets and cannot leave the company's infrastructure — including logs, backups and meeting transcripts. Answers must show their sources and keep measured results separate from assumptions.
What we deployed
An Enterprise AI server on site, with a searchable knowledge system over studies, lab records, batch records and approved publications. Access is separated between research, regulatory and quality teams. Specialized prediction models are kept as a later step, once suitable data has been identified and tested.
Outcome
Researchers can find what is already known about an ingredient before planning an experiment, including earlier attempts that did not work. Quality investigations start from an organized evidence pack. Regulatory staff check submissions for gaps and inconsistencies before sending them. Every answer shows where it came from, and all of it stays on the company's own servers.
Configuration
Enterprise AI tier (2× H100, 256 GB RAM or similar), purchased, with a maintenance contract