The people convening AI for Pharma.
The team behind PharmaOS, already having the conversations the consortium is built around.
Saurabh Moody
He builds AI for pharma's commercial teams, and the consortium is the room he wished existed while doing it.
Saurabh founded PharmaOS and convenes AI for Pharma. Ex-Microsoft, he now works at the seam this consortium is named for: putting AI into the day-to-day of a pharma company's commercial organisation, where sales, marketing, and field-force decisions are actually made, not into a lab or a slide deck.
That work runs on commercial intelligence: the sales, prescription, and market data a brand is measured on, and the systems that turn it into a decision a Brand Manager or a Territory Manager will act on. The recurring lesson, and the reason this room exists, is that the hard part is rarely the model. It is the data foundation underneath it, the review loop around it, and the people who own the workflow it has to fit.
I kept finding myself in two rooms that never met. In one, commercial leaders who knew exactly which number they were measured on, and exactly where their data broke. In the other, the people who could build the AI to move that number, fluent in the model but not in the market share it had to defend.
Almost everything useful happens when those two rooms become one. The model is no longer the bottleneck; the bottleneck is whether the people who own the workflow and the people who can build for it are in the same conversation, using the same vocabulary, looking at the same data.
AI for Pharma is that conversation, made deliberate and recurring. It is the room I wanted while building for this industry. If it is yours too, apply.
Aryan
He ships the AI the room argues about into production, not into a slide deck.
Aryan leads engineering. He builds the parts of an AI deployment that decide whether it survives contact with a pharma commercial team: the data foundations under the sales, prescription, and market feeds, the retrieval and evaluation that make a recommendation trustworthy, and the audit trail that lets a Brand Manager trace a number back to its source.
It is the unglamorous work that separates a demo from a system someone runs every cycle, and it is where most pharma AI pilots quietly stall. His focus is getting them past it, into production.
Ritwik
He reads the commercial org chart the way the people running it do.
Ritwik leads strategy. He works in the operating model of a pharma commercial organisation, the field-force pyramid, the brand and region hierarchies, and the data layers underneath, and in where AI actually changes a decision inside it rather than sitting beside it.
He keeps the consortium's agenda anchored to the questions commercial leaders are really asking, the ones about adoption, attribution, and trust, not the ones that demo well.
The room is who is in it. Be in it.