Everything a launch team asks before they hire us.

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.

Our founders, not an account executive. Chris Bergh, our CEO, takes the first call on a new launch himself. Eric Estabrooks is the senior engineer behind most of our pharma launches and the person you are likely to meet on a Tuesday status call. Gil Benghiat owns the practice, and Chip Bloche sets the engineering bar on every platform we ship.

Yes. Our engineers know NPP, payer claims, specialty pharmacy, syndicated data, and Veeva before they start. A generalist IT team can learn those nuances eventually, but the learning happens on your launch timeline. Nobody spends the first three months finding out what a specialty pharmacy feed is.

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.

Since 2013, and commercial pharma launches since the Celgene days. The company was founded in Cambridge, MA, is bootstrapped, and is fully remote. The founders co-authored the DataOps Cookbook and the DataOps Manifesto, so the methods behind the work are published rather than proprietary.

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.

Partial files that look complete, vendor restatements that read as growth, and territory realignments that make a rep's numbers drop 40 percent overnight. None of those trip a null check, because the data that arrived is perfectly valid and simply incomplete, restated, or remapped. The way you find out is a phone call from the field.

Because you did not create most of it, and the failures are relational rather than local. A prescriber or account record gets assembled from several vendor feeds arriving on their own schedules in their own formats. Identifiers like NPI, NDC, and DEA fail silently and quietly drop rows on the join, so every downstream number comes in low with nothing erroring.

Yes, and it is usually the messiest part of a launch. A specialty brand pulls dispense data from a dozen pharmacies or more, each with its own file, schedule, and format. None of them agree on what a ship date, a fill date, or a patient status means, and each de-identifies patients its own way. Stitching that into one patient journey is the work.

Two checks, not one. Extract-to-load reconciliation counts rows and sums amounts against the vendor's control record, so partial and truncated files stop in staging. Then a snapshot diff compares each period against what the vendor sent last time, which is the check most teams skip and the one that catches a silent restatement before it reshapes a trend line.

Low enough that the field stops asking. A commercial insights leader at Celgene reduced errors to about one per quarter and held that for years. The Otezla launch team shipped hundreds of schema and dataset changes a week with negligible error rates, because tests run at every stage rather than at the end.

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.

Schema and grain, written down. Business definitions for equalized prescriptions, territory alignment, and payer hierarchy. Validated example queries. Live freshness state on every IQVIA or ICON/Symphony refresh. Without it your AI tool guesses at which of 150 tables to join and answers confidently and wrongly, which is worse than not answering.

AI writes the SQL transforms and the data quality tests. AI watches the pipelines and triages failures. AI generates documentation. Our engineers spend their time on what AI cannot do: deciding what to build, designing the right data shape, and talking to your team. The framework behind it is published and open.

With one data source, end to end. Pick the feed the brand team asks about most, engineer the context layer over it, and see what your AI tool answers when it is pointed at the right shape of data. That is a six-week exercise, not a strategy program, and it tells you whether the rest is worth funding.

AI enablement is getting your data engineers productive with AI coding agents such as Claude Code, Codex, or GitHub Copilot on your real Snowflake or Databricks stack, and getting your analysts' AI tools to answer questions about IQVIA, specialty pharmacy, and 867 data correctly. In practice that means isolated environments so an agent cannot break the launch report, tests on every feed so agent output can be graded, and a context layer that defines equalized prescriptions and territory alignment.

Because an agent needs somewhere safe to work and a way to know if it succeeded. Without isolated environments, every iteration touches the tables the field reads. Without tests, neither you nor the agent can tell whether a rewritten specialty pharmacy transform still counts patients correctly. Claude Code writes and runs code quickly. Tests are what tell it the code is right.

Only if the data underneath is verified, and that is the step teams skip. A language model answers with the same confidence whether last week's IQVIA refresh arrived complete or half missing. Trust comes from test coverage under the query: row counts against the vendor control record, freshness on every feed, and checks on NPI and NDC joins. That is why the work starts with data quality rather than prompts.

Give it its own copy to work in. We set up isolated environments with zero-copy clones in Snowflake or Databricks, each with its own Git branch and compute. Your engineers can run three or four AI agent sessions in parallel, and nothing touches the tables your field reports read until an engineer promotes the change and the tests pass.

With one team, one stack, and one data source. The paid two-week scoping engagement leaves you with a working context layer over that source, an AI-readiness scorecard, and a sized proposal. After that the order is fixed: test coverage first so agent output can be graded, an isolated environment so agents can iterate safely, then context for the tables the brand team asks about most.

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 cut its annual data costs by 58%, with better answers because trust went up.

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.

Eventually, maybe, and we will hand the platform to them when you do. Today the hiring cycle runs six to nine months before anyone ships, and specialized commercial pharma data engineers are hard to recruit and harder to keep. Starting now with a team that already knows the feeds buys back the recruiting window.

Because the only lever that model gave you was the hourly rate, and it is spent. Offshoring discounted every manual step in the pipeline and removed none, so the defect rate never moved. We change the factory instead: AI agents write the SQL and about 80% of the tests, and DataOps automation deploys and runs it with tests on every table. That cuts commercial data engineering labor by 70% or more, with fewer errors than the offshore team produced.

Your analysts' week. Data janitorial work absorbs 30 to 50 percent of a commercial analyst bench: reconciling the IQVIA refresh against claims, chasing a payer name that arrived three different ways, rebuilding a territory roll-up after someone changed the alignment file. At Celgene, 10 to 12 analysts covered hundreds of datasets without missed SLAs because the engineering layer held.

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.

Yes. A paid two-week scoping engagement leaves you with a working context layer over one of your data sources, an AI-readiness scorecard, and a sized proposal. If you decide not to move forward, the work is yours to keep. That is the whole point of building in your cloud from the first day.

Usually we take it over rather than rebuild it. Your warehouse stays in your cloud account, your dashboards keep working, and we change what feeds them. Where something has to be rebuilt we say so during the assessment and give you the reason, rather than proposing a rewrite because rewriting is easier for us.

Yes, and it is the normal case. On the BMS CAR-T warehouse, 41 contributors from five companies including Beghou and Accenture worked the same codebase over seven years. That only works when tests are the contract, so a newcomer's change either passes or fails loudly instead of quietly breaking someone else's report.

They should not, because we add tests before we change anything. Coverage goes on the feeds first, so we can prove a change did not move a number. There is no cutover date either: your incumbent keeps running while we take feeds over one at a time, which means there is no single day when everything has to work.

Yes. One product, or even one feed, is a good place to start when the point is to prove the run-rate moves before you commit further. It also keeps the first phase inside a budget you already control rather than one you have to go and ask for.

Because we do not bill hours. The rate is flat, set at scoping, so the work automation takes over is our margin rather than our loss. On an hourly agreement, automating a task deletes revenue, which is why hourly vendors rarely automate one. Our goal is not to maximize consulting dollars, but to build something efficient enough that one to three engineers can run it, with the code in your repository from the first sprint and no exit fee when you want it back.

Because the structure changes rather than the effort. A cost-reduction project ends and the run-rate drifts back. Generated tests do not decay the way a hand-written suite does, volume bounds learned from history do not go stale like fixed thresholds, and a two-stage architecture has no intermediate layers to maintain. Nobody has to police it.

Because it is the line you can move without touching the brand, the field, or the pipeline. Promotional spend and territory coverage protect the revenue that funds everything, and pipeline programs are the company's future. The plumbing under a product that is already selling is where money is freed without anything visible getting worse.

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.

A normal day rather than an event. We transfer to whoever you name: your team, Accenture, or your offshore center. No lock-in, no exit fees. Eisai transferred one of our platforms to Accenture cleanly when they were ready. Because every line of code and every test already lived in their environment, there was nothing to extract.

Yes, and they do. On the BMS CAR-T warehouse, 41 contributors from five companies, including Beghou and Accenture, have worked the same codebase over seven years: 70 datasets, 253,485 lines of code, 594 data quality tests, still in production. That only works when the tests are the contract, so a newcomer's change either passes or fails loudly.

In your cloud account. The warehouse and the data quality testing both run there, and the tests execute as SQL inside your own warehouse, so your rows never cross the wire to us. The orchestration service runs in our cloud and drives your environment without holding your data. For a pharma security review, that split is usually the first thing to establish.

It has, three times. Due diligence scrutinizes whether the commercial story is accurate, auditable, and defensible, and a reconciliation trail on every feed is what turns "trust me" into "here is the check that passed." Our customers' platforms survived every exit and stayed with the customer each time.

Karuna Therapeutics for the Cobenfy launch, Celgene for Otezla and Revlimid, and Acceleron. BMS acquired Karuna for $14 billion and Celgene for $74 billion. Merck acquired Acceleron for $11 billion. The platform stayed with the customer through every one of those transactions.

An average of 1.5 engineers built the whole Cobenfy launch platform at Karuna over two years, covering 50 integrated datasets. At Celgene, seven of our engineers supported $10 billion in US brand sales, and only 18 percent of engineering time went to data loading rather than new analyst work. One pharma cut data costs by 58% when we replaced its setup.

Two posts cover the argument end to end. The $100 Billion Secret explains why pharma companies hand commercial data to a specialized outside team instead of a general IT queue. The cash-runway post works the numbers for a small pharma launching in 12 to 24 months.