AI intelligence that respects how regulated your world is.
Physician targeting, KOL identification, and market-entry intelligence, with every name validated against billing data, not job titles. Built by someone who spent ten years inside Pfizer, with NDAs signed and data structure agreed in writing before anything sensitive is shared.
the case below used public CMS billing data · no patient records touched
0 names invented · every target proven in public CMS Medicare billing
Three jobs I do for healthcare & life-sciences companies.
Commercial intelligence first: the same discipline as the rest of Wallai, tuned to a world where the data is sensitive and the claims have to hold.
Physician targeting & KOL identification
The full universe of physicians who could actually use your product: validated against years of billing behaviour, tiered by real volume, matched to the centers they work in.
10,860 physicians identified for one US medical-device diagnostic · 483 Tier-1, billing-validatedMarket-entry & referral intelligence
Who refers to whom, where the specialty centers cluster, and which territories are underworked, so a launch lands where the volume already is instead of where the org chart guesses.
526 specialty centers matched in the same national buildSafe AI enablement for your team
Which tools are safe for a team that touches sensitive information, plain-language rules your people can follow, and builds with the right architecture, permissions, and human review from day one.
14 days to a first measurable result, the same commitment as every Wallai engagementStraight answers on regulated data.
Most of this work never needs patient data at all: the physician-targeting case on this page was built entirely on public CMS Medicare billing files. When an engagement does touch sensitive information, here's the posture, in plain terms:
If public data can carry the build, that's the build.
The physician-targeting case on this page used public CMS Medicare billing files. No patient records were requested, needed, or touched.
Your compliance people set the boundaries.
If a workflow would touch regulated health information, it gets designed with your privacy or compliance lead inside your rules, or redesigned so the build doesn't need that data at all. The second path is usually available, and it's the one I push for.
Nothing sensitive goes into consumer AI tools.
Patient-level or regulated information never goes into a general-purpose chatbot. Where a build handles sensitive data, the architecture, permissions, and human review are agreed in writing first.
Inpharmativ is Wallai's healthcare & life-sciences division.
Same builder, deeper bench in this vertical: I spent ten of my sixteen corporate-selling years at Pfizer, in commercial roles where the discipline was find the signal, act on it, prove it paid. Inpharmativ carries that discipline into pharma and med-device commercial work. It's the healthcare face of everything on this page.
Steve Walters · Founder · Connect on LinkedIn
Fifteen minutes. One specific answer.
Tell me the product and the market, and I'll tell you on the call whether billing-validated targeting can work for it, and what I'd build first.
Wondering what you'd be signing up for? See exactly what happens on the call.