Mapped in place, not bought back
Semantic transformation maps your sources to a common model where they already sit. Nothing is migrated, which targets the premium you pay for the shape of information you already generated.
Data preparation
You already own the data. You pay three times to use it.
Before any analysis runs, someone has to normalize the data, map it to a common model, strip the identifiers and prove the licenses allow the use. Today each of those is a separate invoice from a separate supplier - and the work is thrown away at the end of the study.
Mapped in placeDe-identified before useEntitlement enforced
What we have to offer
Three parts. None of them is the product on its own - a workforce without governed execution is a generic agent platform, and governed execution without your data and your authority is somebody else’s system.
One - the work
Performed, not tooled.
Biostatistics, medical writing, regulatory, safety and HEOR executed end to end by governed specialists you scale without hiring, built for pharmaceutical work rather than adapted to it.
Two - the proof
Proven while it runs.
Policy is evaluated before a task is dispatched, not reviewed after it lands, and every result arrives with the evidence record that defends it.
Three - the authority
Under your authority.
Your data and your models, deployed where you choose - managed, in your VPC, embedded, or air-gapped - with every decision right resolved to a named person in your organization, never to the platform.
The displaced cost
These are not platform features. They are line items StromaOS is designed to reduce or displace.
Data preparation is part of the work, so StromaOS performs it rather than requiring you to buy it first. The distinction matters to a CFO: this is spend that moves, not capability that is added.
PHI and PII removed before use
De-identification and anonymization run as governed steps at the point of preparation, with the record of what was protected, and how, attached to the run.
Your licenses, verified and enforced
MedDRA, WHODrug and the rest. Entitlement is checked before a licensed asset resolves, and a run that loses entitlement withholds its result rather than destroying the work.
The mapping is an asset, not a service
Once your data is mapped, it stays mapped. Reusable preparation can reduce what each subsequent study must repurchase, which is what bends the cost curve after the first.
Three line items you pay for today are designed to become steps in the work rather than invoices in front of it.
Your data stays where it is
Connect the sources. Do not migrate them.
Systems of record hold what you did. StromaOS performs the work those systems govern and writes results back where your organization already looks.
Systems of record
Content, quality and safety platforms stay exactly where they are. We do the work and hand the record back with the evidence attached.
Warehouses and lakes
Heavy analytical work is pushed down to run where the data already sits, rather than extracting it to us.
Licensed sources
Terminologies and external datasets resolve under your entitlement, not a resold one. Your license, your relationship.
Governed preparation
AI can accelerate data preparation without moving your data.
Preparation is where most governance failures originate, because it happens before anyone is watching. Every step here is a governed action with a Sentry check in front of it and a record behind it.
A mapping decision is a scientific decision. Which fields are equivalent, which units convert, which codes are synonyms, what counts as missing rather than zero - these determine what an analysis can conclude.
Treating that as invisible plumbing is how a study becomes unreproducible two years later. Treating it as governed work is how it stays defensible.
01
Mapping decisions are recorded
Every transformation carries its rationale, its version and the person or policy that authorized it.
02
Classification travels with the data
A worker cleared for de-identified data cannot reach identified data, at any step, including preparation.
03
Entitlement is checked before resolution
Not audited afterwards. A licensed asset does not resolve without a valid entitlement.
04
Unknown is never recorded as none
A source that could not be reached says so. A check that did not run is never recorded as passed.
Ingestion and retrieval
Getting evidence in is part of the work, and it is governed too.
Most of what a scientific question needs is not in a warehouse table. It is in protocols, study reports, literature, labels and correspondence. The catalog carries the ingestion, retrieval and extraction capability to reach it, so a Fleet doing real evidence work is not waiting on a data engineering project first.
Pipelines, cleaning and orchestration
Governed ETL, tabular cleaning and transformation, and API orchestration across the systems you already run - plus the domain ingestion that scientific work needs, including bring-your-own-licence source data and vocabulary ingestion with its own quality checks.
Retrieval-augmented generation, under policy
Retrieval against your documents rather than a general model’s memory. Entitlement is verified before a passage is retrieved, and provenance is retained at the passage level, so every assertion traces back to the source it came from and a use your licence does not cover is refused rather than answered.
Documents, tables and multimodal sources
Structured extraction from PDFs, forms, tables, audio and video - the formats regulated evidence actually arrives in. Extracted values carry their source location, so a reviewer can go from a number in a report back to the page it was read from.
Profiling, anomalies and quality
Data profiling, anomaly investigation and quality checking run as governed capabilities in the same catalog, so the checks on a dataset are part of its record rather than a script someone ran once.
None of this moves your data. Ingestion, retrieval and extraction execute inside your boundary under the same Governance Spine as the analysis they feed, and produce the same Evidence Data Trace.
A source graded, not assumed
Fifty percent, shown rather than smoothed. Look at the first tile: validity reads “Not evaluated”, not zero. A system that records unknown as none will eventually tell you your evidence is strong when nobody checked. Every open issue names the rule that failed, the dimension it belongs to, and how many records it reaches.
Captured from a running instance. Demo tenant on synthetic and public test data.
Shared meaning
One vocabulary across every domain, without moving your data.
A governed semantic runtime, not another golden record. No data moves - only meaning flows. The alternative is the migration project that consumes the budget before anything is produced.
Targets, mechanisms, assays
Terminology reconciled across programs so early evidence is comparable rather than merely adjacent.
Endpoints, populations, visits
Study definitions mapped to a common model so analyses can span programs without hand-reconciliation.
Events, products, obligations
MedDRA and WHODrug resolved under your entitlement, consistently, wherever they are referenced.
Batches, deviations, specifications
Manufacturing and quality vocabulary connected to the clinical and regulatory record that depends on it.
Comparators, outcomes, costs
Economic models built on the same definitions the clinical evidence used, so a payer challenge does not require a rebuild.
Insights, enquiries, evidence gaps
Field and scientific communication connected to the approved evidence base rather than a parallel copy of it.
Your organization’s vocabularies combined with scientific and industry ontologies - not one imposed over the other.
Knowledge Management
Every execution enriches what the next one knows.
Connected meaning is what makes multi-hop reasoning possible - following evidence across domains rather than running isolated searches and stitching the answers together by hand.
A search index over your old documents is a filing cabinet with a search box. It can find a document. It cannot tell you which analysis produced the number inside it, or which decision that number went on to support.
Connected knowledge accumulates as a durable asset scoped to your boundary - not a model somebody else improves using your work.
01
Scientific and clinical knowledge
Targets, mechanisms, endpoints and populations, connected across programs.
02
Regulatory and quality knowledge
Obligations, commitments, precedent and the evidence that satisfied them.
03
Execution knowledge
Which methods were used, which were refused, and what a reviewer flagged last time.
04
Organizational knowledge
Your SOPs, house standards and reviewer expectations - the part that usually leaves with people.
Why it compounds
The preparation you pay for once keeps working.
The mapping is built and recorded
Sources harmonized, identifiers removed, entitlements verified - performed as part of the work rather than purchased ahead of it.
It starts from the mapping, not from zero
The same sources are already harmonized. What changes is the question, not the plumbing underneath it.
The cost curve bends
Retained preparation is what bends the cost curve after the first study - unlike project-based services that commonly reset the preparation effort.
Who owns the normalized data - you, or them? It is the question worth asking any vendor in this category, including us. Ours stays yours.
Where to start
Start with one dataset you are paying someone else to prepare.
Bring the invoice lines for normalization, de-identification and license administration to a working session, and we will model what moves and what does not.