for Pharma
Commercial Pharma
The data platform behind three pharma launches acquired for $100B. Built in your cloud. Yours when you say.
Trust · Speed · Value · Yours
Track record
Nearly $100 billion in acquisitions.
The platform survived every exit. Stayed with the customer every time.
From the work
We reduced errors to just about one per quarter. It's been several years since we've had any major glitch.
Both sides of AI
Most vendors do one or the other. We do both. Your analysts hold the business. We hold the engineering.
On our side
AI writes the data quality tests. AI watches the pipelines. AI triages failures. AI generates documentation. Our engineers spend their time on the parts AI can't do: deciding what to build, designing the right shape, talking to your team.
Framework: DataOps + FITT + Data Testing = 10xOn your side
Your Snowflake Cortex Analyst or Databricks Genie won't work until the data underneath has context. We engineer that layer. Schema and grain. Business definitions for equalized prescriptions, territory alignment, payer hierarchy. Validated example queries. Live freshness state on every IQVIA refresh.
Framework: DT + DX + CTX = 10xWhat you get
Three customers acquired by BMS and Merck for nearly $100 billion combined. Field reports working six months before launch. A small pharma cut data costs by two-thirds when we replaced what they had. The platform lives in your cloud, in your repo, and transfers to your team when you say.
The math
Most pharma data teams spend 80% of their time on plumbing. AI writes our tests, watches our pipelines, and triages failures, so our engineers spend their time on what AI can't do: decide what to build, design the right data shape, sit in your standup.
On our side
DataOps + FITT + Data Testing = 10xThe framework behind how a tiny team ships fast without breaking things. DataOps is the method. FITT data architecture (functional, isolated, tested, two-stage) is the engineering discipline. AI-generated tests are the safety net. Open. Published. Used by us and by customers running our software on their own.
On your side
DT + DX + CTX = 10xData Trust, Data Experience, Context Engineering. The three things your AI tools need to answer real business questions. Without any one of them, your AI tool guesses.
Both frameworks are open and published. Both are used by customers running our Apache 2.0 software without us.
What we are
Small embedded team. Open code. Yours when you say.
How we work
Three phases. Clear roles. Nothing is a black box. We hold the engineering lane and you hold the business lane. The platform transfers to your team when you say.
Build
Months minus 18 to minus 1
We build the commercial data platform your launch needs. Star schemas your analysts can use. The AI-ready context layer next to them. Tests on every table. Observability across every pipeline. Field reporting six months before launch.
Delivers: A production platform six months before launch day.
Run
Launch day through year one
We're in your standup. We ship new datasets in days. We fix what breaks before the field sees it. Your analysts spend their time on analysis, not data cleanup. Your Snowflake Cortex Analyst or Databricks Genie answers real questions because the data underneath it is real.
Delivers: Launch hit. AI working in production. Analyst queue under a week.
Transfer
Whenever you say
You own it from day one. The platform sits in your cloud. The code is in your repo. When you want to take it over, we transfer to your team. Or to Accenture. Or to your offshore center. No lock-in. No exit fees.
Delivers: A working platform in your hands, your team trained, our people gone.
Daily
Scrum and ticket triage. Everything tracked in Jira. Full visibility into work in flight.
Weekly
Status meeting with your analytics lead. Review work completed, in progress, and planned.
Quarterly
Business review with leadership. Highlights, project timelines, metrics on bugs, tests, datasets.
Everything tracked in Jira. Documentation in a shared wiki. Time tracked and reported. Visibility is the default.
What we deliver
The Commercial Data & Analytics Platform shows up four ways across launch. Different teams ask different questions. The platform stays under your control.
We don't do IC, alignment, call planning, or brand strategy. We do the commercial data layer underneath all of them.
When you're launching
Brand and sales change tactics fast during a launch. We build the integrated Commercial Data & Analytics Platform under the brand team's control. You iterate on analytics without breaking data quality. You spot sub-national anomalies as they show up.
When share moves region by region
Managed Markets teams spend millions on data, systems, software, and ad-hoc consulting. Our platform integrates the data that moves share. Which major model groups rank highest. How brand performance tracks regionally by payer. Which payers are most influential. How formulary status shifts share.
When specialty networks matter
Specialty therapy revenue runs on knowing your network. You find new prescriber opportunities and catch approval roadblocks early. You measure payer-contract ROI and patient adherence. You drive access for local reimbursement and formulary placements.
When digital channels need ROI
Digital channels disrupted the industry. NPP data sources now cover every customer interaction. We deliver an NPP Commercial Data & Analytics Platform under the analytics team's control. You measure marketing ROI. You make better budget decisions across channels. You see every channel in one view of the customer.
Read more
Case Study
How 1.5 of our engineers built and ran the Cobenfy launch platform that BMS bought for $14 billion.
Read the case study →
Blog
Why leading pharma companies have outside teams run commercial data: specialized focus, speed, and transparency over generalist IT.
Read the article →
Webinar
James Royster, Karuna Therapeutics, on the agility, efficiency, and aphorisms that make commercial pharma analytics teams work.
Watch the webinar →
Tell us about your launch. We'll show you what a tech-enabled commercial pharma data team would do on your stack, your data, your cloud.
Trust · Speed · Value · Yours