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Commercial Pharma

Commercial pharma data engineering, built for your launch.

Your team is launching a brand. Your CEO wants AI fast. We engineer the data platform behind both. Built in your cloud. Yours when you say.

Contact us See how we work

Trust · Speed · Value · Yours

Track record

Nearly $100 billion in acquisitions.

The platform survived every exit. Stayed with the customer every time.

  • BMS acquired Karuna · Cobenfy $14B
  • BMS acquired Celgene · Otezla, Revlimid $74B
  • Merck acquired Acceleron $11B

From the work

We reduced errors to just about one per quarter. It's been several years since we've had any major glitch.

Rajesh Gill · Director of Commercial Insights, Amgen (formerly Celgene)

Both sides of AI

AI on our side. AI-ready data on yours.

Most vendors do one or the other. We do both. Your analysts hold the business. We hold the engineering.

On our side

AI-Powered Data Engineering

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 = 10x

On your side

AI-Ready Data

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 = 10x

What you get

Trust, Speed, Value, Yours

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

Well engineered systems, with AI, cover hundreds of data sets with perfect quality on a tiny team.

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 = 10x

The framework behind how a tiny team ships fast without breaking things. DataOps is the method. FITT data architecture (functional, idempotent, 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 = 10x

Data 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

We engineer. We do not consult.

Small embedded team. Open code. Yours when you say.

  • Engineers in your standup, not contractors in a SOW
  • Apache 2.0 software underneath, no proprietary lock-in
  • Transfer to your team on your timeline

How we work

Build. Run. Transfer.

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.

DataKitchen

Engineering & quality

  • Code and build the data warehouse
  • Implement data features
  • Ensure process and data quality
  • Run weekly status meetings
  • Stand up infrastructure: cloud database, DataOps automation, observability, SFTP
Your team

Analytics & the business

  • Analytics, dashboarding, reporting
  • Prioritize data features
  • Work with business users
  • Overall project management
  • Master data management

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

Same platform. Four jobs.

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.

Product launch

When you're launching

Product launch

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.

Managed markets

When share moves region by region

Managed markets

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.

Specialty pharmacy

When specialty networks matter

Specialty pharmacy

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.

Non-personal promotion

When digital channels need ROI

Non-personal promotion

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 studies, webinars, and writing from the pharma practice.

All resources

Drug Launch Case Study: Amazing Efficiency Using DataOps

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 →

The $100 Billion Secret

Blog

Why leading pharma companies have outside teams run commercial data: specialized focus, speed, and transparency over generalist IT.

Read the article →

Driving Data Analytic Team Excellence

Webinar

James Royster, Karuna Therapeutics, on the agility, efficiency, and aphorisms that make commercial pharma analytics teams work.

Watch the webinar →

From the blog

Latest on commercial pharma data.

All pharma posts
IQVIA, specialty pharmacy, and your own files: the commercial data most likely to be wrong
Why Testing Commercial Pharma Data Is Harder Than Anyone Tells You
Hoarding cash before the FDA gate opens? Your data and analytics bill is the easiest line to cut.

Questions

What launch teams ask us first.

The short answers. The full set, including cost, transfer, and security, is on the FAQ page.

What does DataKitchen do for commercial pharma?

We engineer the commercial data platform behind a pharma product launch. That means integrating the vendor feeds, building star schemas your analysts can actually query, engineering the context layer your AI tools need, and running tests on every table. Our engineers sit in your standup through launch day and the first year. The platform runs in your cloud and transfers to your team when you say.

Who do you work with?

Two situations. Small and mid-size pharma with a launch 12 to 24 months out, where the shape is one or two assets and a commercial data lead who has not launched a brand before. And companies with a product already selling that need the data run-rate down to fund the next one. Three of our customers were later acquired by top-five pharma for $100 billion combined.

How is this different from ZS, Beghou, or Accenture?

Analytics consultants sell analyst hours, billed at around $300 each, and hand you decks. Accenture and Deloitte bring a 30-person engineering team and a statement of work. We send one to three engineers who write code in your repository. The endgame is different too: they keep the work, we hand it over. So is the incentive. An hour is their product, so more hours is a better year. Ours is a flat rate, so a leaner operation is.

How many engineers do you put on a launch?

One to three, typically. Roughly 1.5 engineers built the entire Cobenfy launch data platform at Karuna over two years, work most pharma companies staff with 10 engineers. At Celgene, seven of our engineers supported a $10 billion-a-year commercial portfolio. Small senior teams backed by automation, not a bench of generalists.

How far before launch should we start?

For a launch, 12 to 24 months out is cheapest and calmest: the first 6 to 12 months set the drug's lifetime revenue trajectory and you get one chance at the field's trust. If a product is already selling, timing works differently. We take the existing operation over feed by feed, so there is no window you can miss.

What data sources do you work with?

IQVIA, ICON/Symphony, Veeva, specialty pharmacy dispense feeds, medical claims, non-personal promotion, real-world evidence, copay, and EDI 852/867. There is no learning curve on any of them. We work with the data you already receive rather than asking you to standardize it first.

Will this make Snowflake Cortex Analyst or Databricks Genie work?

Not on their own, and not until the data underneath carries context. When MIT's NLP group tested leading text-to-SQL models on the Spider 2.0 benchmark of real enterprise databases, accuracy fell from over 85 percent on academic benchmarks to under 20 percent. The model was not broken. The data underneath it had no context.

What does this cost?

A flat monthly rate, set at scoping. For comparison, six fully loaded commercial data FTEs in Boston or the Bay Area run $1.5 million to $2 million a year, plus a six to nine month recruiting cycle. One pharma replaced its existing setup and went from $1 million a year to $300,000, with better answers because trust went up.

What happens if our launch slips?

The rate scales down with you on 30 days' notice, with no penalty for an FDA delay you did not choose. That matters when roughly 3,500 staff left the FDA after the 2025 cuts and biotechs are missing meetings and pushing trials. A fixed multi-year commitment is the wrong shape for a date you do not control.

Do we own the platform and the code?

Yes, from day one. The warehouse sits in your cloud account and every line of code, test, and schema lives in your repository. The data quality software underneath is Apache 2.0, so there is no proprietary runtime to leave behind and no license that expires when we do. You are not renting access to something we hold.

How do we get started?

Two meetings. The first is 30 minutes: you describe the launch, the team, the data sources, and the timeline, and we tell you whether we are a fit. If we are not, we say so and recommend who is. The second is 60 minutes walking one of your data sources end to end on your screen.
All questions

Both sides of AI. One engagement. Yours when you say.

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.

Contact us

Trust · Speed · Value · Yours