Solutions · Pharma & Life Sciences
Rich, live context for pharma AI, from any kind of data.
A safety narrative, a lesion image, a claims record, and a paper published this morning each carry meaning your AI needs: what it says, where it came from, who may use it, and how current it is. Nextdata OS keeps that context attached and up to date across every format, so agents can take on work that schema-first stacks can’t support.
The problem
Most of what matters in pharma never fits a table.
Conventional stacks are schema-first: everything has to be modeled into rows and columns before anything can use it. That works for claims and sales. It fails for the protocol, the case narrative, the image, and the new paper, which is where most of the signal lives. And what does make it through arrives stripped of its meaning, its source, and its permissions, usually a day or more behind.
Lost on the way in
Left out entirely
Why it’s hard
You can’t bolt context back on afterward.
Teams try. A catalog describes the tables, a vector store holds the documents, a semantic model sits behind each dashboard, and nightly jobs refresh it all. Each piece holds a fragment of the context. None of them enforce it, and none are current when an agent asks. So the model reasons over a stale, partial picture, gives different answers to different people, and can’t show its sources. In pharma, that’s where AI stalls: not at the model, but at what the model gets to see.
Teams try.
A catalog describes the tables
A vector store holds the documents
A semantic model sits behind each dashboard
Nightly jobs refresh it all
Each piece holds a fragment of the context. None of them enforce it, and none are current when an agent asks.
The solution
Context that stays attached, in any format, as it changes.
Nextdata OS turns every source (tables, documents, images, and feeds) into autonomous data products. Each one keeps its context with it: definitions, provenance, permitted-use policy, relationships to other products, and freshness. Products update as their sources change, and agents reach them through standard interfaces, including MCP. Nothing has to be forced into a schema first, and nothing leaves the systems you already run.
01 / Any source, any format
Tables & records
Claims & EHR patient journeys
Registries & outcomes
Assay & biomarker data
CRM & field activity
Snowflake · Databricks · Azure · AWS
Documents, images & feeds
Literature & preprints
Protocols & clinical notes
Safety narratives & case reports
Medical images
MSL & field notes
No schema required up front.
02 / Nextdata OS
Context-rich data products
Updated as sources change. Linked across formats.
Live context layer
What · Where from · Who may · How fresh
Attached to the data, not rebuilt downstream. Current at the moment an agent asks.
03 / Every consumer
Agents, models & analysts
Reason across every format
Answers show their sources
Policy checked on every use
Shared use cases
FIG. 1 — Every source keeps its context (meaning, provenance, permissions, freshness) and serves it live to any agent, model, or analyst.
01 · Any source
Tables & records
- Claims & EHR patient journeys
- Registries & outcomes
- Assay & biomarker data
- CRM & field activity
Documents, images & feeds
- Literature & preprints
- Protocols & clinical notes
- Safety narratives & case reports
- Medical images
- MSL & field notes
02 · Nextdata OS
Context-rich data products
Clinical
Safety
Commercial
STRUCTURED · UNSTRUCTURED · LINKED ACROSS FORMATS
Live context layer
Attached to the data, not rebuilt downstream. Current at the moment an agent asks.
03 · Every consumer
Agents, models & analysts
Answers show their sources
Safety signals
Patient finding
Trial design & cohorts
Responder analysis
How it works
Any format, no schema first
Tables, documents, images, and feeds are usable as they are. Nothing has to be modeled into rows before AI can reason over it.
Context that stays attached
Meaning, provenance, and relationships travel with the data, so an agent knows what a record says and where it came from.
Current when it’s asked
Products update as their sources change, so an agent reasons over this morning’s case reports, not last week’s extract.
Governed at every use
Permitted-use policy is checked each time a person or agent uses the data, and every answer traces back to its sources.
What it unlocks
Use cases schema-first stacks can’t deliver.
Each depends on context from more than one format, and most depend on it being current.
Safety signals
Signals show up in narratives, literature, and case reports before structured fields catch up. Agents assemble and rank them continuously, with every source traceable for reviewers.
Patient finding
Delayed diagnosis shows up across claims, referrals, and clinical notes together. Find the pattern with permitted-use policy checked on every query.
Trial design & cohort exploration
Test inclusion criteria against patient data and the latest literature in one pass, instead of waiting on a bespoke extract.
Responder analysis
Link biomarkers, images, and outcomes at the patient level to see who responds, including the evidence that never fit a table.
Why now, why you
Top-10 ambition, without a top-10 platform team.
Mid-size and emerging pharma carry the same AI mandate as the majors, without the headcount to model every source into a warehouse first. Nextdata adds the context layer on top of the clouds, warehouses, and lakehouses you already run, so the documents and images you couldn’t use before become usable, without a migration.
Proof
Built by the team behind data mesh. Proven in pharma.
Nextdata was founded by Zhamak Dehghani, who created data mesh. At a commercial-stage immunology biotech, Nextdata connected unstructured scientific literature with structured patient data for trial design, gave analysts and agents the same answer to the same question, and took four AI use cases from stalled to live in under a year. The work is now expanding from commercial and clinical research to finance, tech ops, and supply chain.
Zhamak Dehghani
Founder and CEO
Cornelius Willis
Head of Marketing
Jörg Schad
Head of Engineering
Sina Jahan
Head of Product Engineering
Select customers
Customer Perspective
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Customer Name
Title, Pharma Company
Next step
Bring one AI use case that’s short on context.
In 20 minutes we’ll map which sources it needs, what context they lose today, and what it would take to give your agents the full picture.
