We spent years engineering systems for ecommerce and UX. We are now applying that discipline to pharma, where being right matters more than being fast.
Why we exist
Most AI never leaves the demo. Pilots stall in review, models sit behind a login nobody opens, and the deck outlives the system. We exist to close the gap between a promising model and something your team actually uses on a Tuesday. Evaluated, observable, and shipped into the workflow it was supposed to fix.
What we do
Regulated work demands more than a right answer. It demands a path back to why. We build systems where every output can be traced to the evidence underneath it.
Every catalog, cart, and customer signal is training data. We turn it into systems that decide better than a rules engine ever could.
Weeks of synthesis compressed into hours, without flattening the thing that made the research worth doing. Design teams keep the judgement. We remove the transcription.
The connective tissue. Retrieval, orchestration, and evaluation that make every other sector compound instead of fragment.
How we work
Work, not claims
Some of this is client work we cannot name. The rest we built ourselves, so we can show you exactly how we work.
A RAG system for a pharmaceutical client, built so every answer carries a path back to the document it came from.
Turns deviation narratives and source records into structured, reviewable reports, where every claim links to the evidence behind it and unsupported conclusions get flagged rather than smoothed over.
Tools for cancer researchers, aimed at the slow manual middle of the work rather than the part that makes headlines.
A virtual population and pharmacokinetic simulator, so trial design decisions can be pressure tested before anyone is enrolled.
Want a closer look? Ask us for a demo or a detailed write-up.
“We would rather show you an unfinished system than a finished slide.”
Next step
Tell us where your workflow breaks. We’ll show you where intelligence fits.
hello@zqas.io