In market

Cut the data run-rate on the product you already sell.

You have a product in market paying the bills and another one coming. The job is to free cash for the launch without touching the brand, the field, or the pipeline. The plumbing under the product that is already selling is where that money is, and it is usually the largest line nobody has looked at.

We take over an existing commercial data operation and run it for about a quarter of what it costs today. Our goal is not to maximize consulting dollars, but to build something that is efficient.

Run the numbers with us See the arithmetic

Trying this does not spend your powder

Flat monthly rate: Set at scoping, not metered against hours.
Thirty days notice: Scales down with you, with no penalty for a delay you did not choose.
No exit fees: Transfer to your team, to Accenture, or to your offshore centre.
Already yours: The code is in your repository from the first sprint, so leaving costs you nothing you have not already got.

Out of scope

What we do not touch.

This is not a commercial budget exercise. Everything that protects revenue or the pipeline stays exactly where it is.

Brand spend and promotional budget
Field headcount and territory coverage
Your pipeline programs and launch prep
Your syndicated data contracts
Your BI tool and the dashboards people already use

The arithmetic

Where the money is going, and what happens to each line.

Note which line does not move. The syndicated data contracts are the biggest number and the one everybody assumes is the target. They are not. The cost is in what it takes to run the data, not in buying it.

Commercial pharma data cost lines today versus after DataKitchen takes the operation over
Line	What you pay for today	After
People	A bench of commercial data engineers, or an offshore pod, or both, plus whatever internal IT time gets charged against the brand.	One to three of our engineers. The structural change is that automation carries the load a headcount count used to.
Consulting retainer	A multi-year analytics agreement billing analyst hours at around $300, which never transfers anything back to you.	Gone. A flat monthly rate set at scoping, with the platform in your repository from the first sprint. The rate does not go up because the work took longer.
Software	Per-seat data quality and observability licences layered on top of the warehouse you already pay for.	Apache 2.0 underneath, running in your cloud. No per-seat licence and nothing that expires when we leave.
Syndicated data	IQVIA, Symphony, specialty pharmacy, claims. The largest line, and the one everybody assumes is the target.	Unchanged. We work with the feeds you already buy. This is not a data-contract renegotiation.
Analyst time	Thirty to fifty percent of your analyst bench doing data janitorial work at a fully loaded analyst rate.	That time comes back to analysis. It is the saving that never shows up as a line item and costs the most.

The documented case

One of these has already happened.

Fewer than a hundred patients on therapy, a second indication in phase III, and a finite runway between the two. The data operation was the one line he could move without touching the brand or the pipeline.

“We have a Cadillac. We need a Corolla.”

VP of technology, rare-disease biotech

58% lower

annual cost of running commercial data

Measured against the ten reports the operation had already been cut back to, not the forty-one it started with. By the customer’s own count the other thirty-one were not being read, so this compares a real workload against a real workload rather than a full operation against a gutted one.

What he was paying for

Two vendors, six to eight people between them, billing by the hour and working out to three or four full-time equivalents. One owned ingestion and the warehouse, the other owned the datamarts and the reports.
Snowflake averaging a bit over $700 a month, AWS a few thousand more. The infrastructure was never the cost.
A warehouse about two years old, of which at most 40% could be reused for the second indication.
One report showing an email open rate of 181% that nobody had flagged, and an analyst on the commercial team quietly rebuilding the numbers out of the CRM.

What replaced it

Thirty days of planning, then an eleven-week fixed-price implementation covering everything between the vendor SFTP drop and the reports.
A standing team of about two: one on operations, one on automation and improvement. The second person is the argument, because a managed service with no budget line for making things better only ever gets more expensive.
Annual cost of running commercial data down about 58%, with monthly hours still falling after the second indication’s data work was added on top.
At the most recent quarterly review his own summary was that quality fixes get operationalized instead of patched for the month. That is why the saving holds instead of running out.

The answers got better while the cost came down, because reconciliation ran on every feed instead of the ones somebody remembered to guard. Read the full customer story: why the cloud bill was never the problem, what cutting reports did and did not save, and what the savings freed up for the next launch.

Our incentive

Efficiency, not consulting dollars

Ask how a vendor gets paid before you believe what they tell you about cost. An hourly agreement pays more for taking longer, staffing wider, and keeping what it learns. Ours pays the same either way, so the cheapest operation we can build is the one we want to build.

Automation is our margin

The rate is flat, set at scoping. Every test AI writes and every failure the platform triages by itself is our gain too. On an hourly agreement those same hours are revenue somebody has to protect, which is why hourly vendors rarely automate them away.

We cannot hold the operation

The code is in your repository from the first sprint, on Apache 2.0 software running in your cloud. There is no version of this where we know something you do not and charge you for the gap.

The endgame is handing it back

Transfer to your team, your offshore centre, or another vendor, with no exit fee. A firm whose revenue depends on staying does not write that sentence down.

The objection

Cheaper usually means riskier. Here it does not.

The product in market is funding everything, including the launch you are saving for. If cutting its data cost made its numbers shakier, the trade would not be worth making. It does the opposite, and that is the whole reason this works.

Trust goes up, not down

At the pharma where we replaced the existing setup, costs fell by roughly three quarters and the answers got better, because reconciliation on every feed meant the numbers held when someone challenged them.

The in-market product is the priority

It is paying for everything, including the pipeline. Seven of our engineers supported $10 billion in US sales across Revlimid, Otezla, and Immunology, with 10 to 12 analysts covering hundreds of datasets and no missed SLAs.

Fewer people, more coverage

Only 18 percent of engineering time at Celgene went to data loading. The rest went to new analyst work, which is what a bench of loaders never gets to.

How a takeover runs

Assess. Take over. Run leaner. Ready for the next one.

No cutover date. We take feeds over one at a time with tests on them first, so there is never a single day when everything has to work.

Assess

We measure what you are running now: which feeds arrive, what they cost, what tests exist, and which reports the field actually depends on. You get a coverage number and a finding list you did not have before, whether or not you go further.

A defensible picture of the current operation.

Take over

We inherit the platform feed by feed, tests first, so coverage exists before anything changes underneath it. Your incumbent can keep running while we do it. Nothing gets switched off on a date somebody picked in a plan.

Tested coverage on the feeds that matter, with no cutover event.

Run leaner

One to three engineers operate what a bench plus a retainer used to. The load is carried by generated tests, observability that triages its own failures, and a FITT architecture that does not need a person awake at 2am to survive a late vendor file.

A lower structural run-rate, not a one-time cost exercise.

Ready

The platform now running your in-market product is the one your next launch plugs into. The feeds are already tested, the dimensions already modelled, the team already knows your data. Launch prep stops being a second capital project.

You do not pay twice.

Your existing setup

Nothing you have built gets thrown away.

Your warehouse: Stays where it is. We work inside your cloud account rather than moving your data somewhere we control.
Your dashboards: Keep working. We change what feeds them, not the BI layer your users have already learned.
Your data contracts: Untouched. Same vendors, same files, same delivery schedule.
Your incumbent vendor: We can run alongside them rather than replacing them on a cutover date, and hand back to them or to you later.
Your scope: Start with one product, or one feed, and widen once the run-rate moves.

Does it last

A savings project ends. A lower run-rate does not.

A consultancy runs a cost-reduction project and leaves, and the run-rate drifts back within a year.

We change the structure instead: generated tests rather than a hand-written suite that decays, learned volume bounds rather than thresholds someone comments out, and a two-stage architecture with nothing in the middle to maintain.

Nobody has to police the saving for it to persist.

The incentive points the same way. Nobody here gets paid more for the operation needing more hands next year.

The part nobody budgets for

You do not pay twice.

Most companies fund the in-market data operation and then fund a second capital project to build a launch platform beside it. The platform running your marketed product is the one the next launch plugs into: feeds already tested, dimensions already modelled, a team that already knows your data. Launch prep stops being a separate line.

What the launch side looks like

Questions

About taking over what you already run.

Why cut data spend rather than something else?

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.

You bill us too. Why would you cut your own revenue?

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.

We already have a platform. Do you replace it?

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.

Will our reports break while you take over?

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.

Can you work alongside our current vendor?

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.

Can we start with one product instead of everything?

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.

How do we know the savings last?

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.

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 cut its annual data costs by 58%, 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.

All questions

What are you spending now?

Thirty minutes. Bring the current run-rate, the feeds, and who is running them today. We will tell you what we think it could cost instead, and if we are not the right answer we will say so and recommend who is.

Run the numbers with us

Trust · Speed · Value · Yours