AI-ready data products for pharma and biotech R&D
Turn internal, public and third-party data into a queryable resource built around your scientific questions.
Ardigen sources, ingests, processes, harmonizes and curates complex omics, imaging, pathology and clinical data into a findable, queryable data product. Delivered within the environment you already use, it gives scientists direct access and supports AI-enabled research workflows, including governed access for AI agents through an internal data MCP where appropriate.
The scientific question is clear. The data is not ready to answer it.
Life sciences organizations already hold valuable data across internal systems, public repositories, publications and third-party providers. But each source arrives with different formats, metadata, naming conventions and levels of structure. Connecting an AI agent to these sources through MCP does not resolve the underlying inconsistencies, missing context or incompatible schemas. Before scientists or agents can query the data effectively, specialist teams may spend months sourcing, processing, checking and reconciling it. A useful private data MCP therefore depends on a curated data product underneath it.
Where the work gets stuck
The data exists, but not as one usable resource
Omics, imaging, spatial, pathology and clinical data often remain split across files, repositories and programs. Scientists cannot quickly see what is available, how it relates or whether it is ready for a particular analysis.
Preparation competes with research
Computational biologists and research informatics teams repeatedly perform manual ingestion, quality control, filtering, re-aggregation and metadata mapping. Time spent rebuilding data context is time not spent investigating disease biology, targets or therapeutic hypotheses.
Every new modality creates another first
Emerging modalities require more than generic data engineering. Without experience in the assay, data structure and intended analysis, teams face slow delivery, uncertain integration and workflows that are difficult to scale.
The platform is present, but the data product is missing
A lakehouse, cloud environment or curated external corpus does not automatically make proprietary data usable. The unresolved work is often blending internal and external sources into a product that fits the organization’s science, standards and downstream workflows.
The missing layer between infrastructure and science
As more data arrives, the technically understood volume grows faster than the queryable volume. Investments in generating, acquiring and storing data remain only partly used, while important scientific questions stay difficult to ask.
Ardigen builds the operational data layer between your sources and the science they need to support. We combine internal, public and third-party data; apply the required structure, quality controls and scientific context; and deliver the result as an organized body of knowledge for scientists and AI agents.
The product is designed for your modalities, standards, analytical workflows and technology environment, rather than a generic schema that forces your data to fit someone else’s roadmap. Where agent access is required, we can expose the curated product through a private MCP layer designed for the organization’s own data and tools.
This data operationalisation work creates agent-ready biomedical data products: structured, contextualized assets that can support scientific analysis, AI models and governed agent workflows.
How it works
How it works
Start with the scientific use case
Define the questions, downstream analyses, users and access requirements the data product must support.
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Bring the sources together
Ingest selected internal, public and third-party data into a reproducible workflow.
Make the data consistent and trustworthy
Harmonize formats and metadata; perform quality control, filtering, normalization and ontology mapping as required by the modality and use case.
Deliver it where the work happens
Integrate the data product with the organization’s existing cloud or data environment and provide suitable access through notebooks, APIs or an internal MCP server for AI agents.
Configure repeatable pipelines so new or updated data can be incorporated without rebuilding the product from scratch.
Built for the modalities and sources you need
Imaging and screening data
Cell Painting data curation, high-content screening data harmonisation, multiplexed tissue imaging data curation, multiplexed imaging data harmonisation and optical pooled screening data curation.
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Omics processing and integration
CRISPR screen data harmonisation, DRUG-seq data processing pipeline development and single-cell atlas integration at scale.
Metadata and source automation
Ontology mapping for omics metadata, GEO ingestion automation and blending internal and public omics data into a reusable product.
Turn more of your data investment into scientific progress
Benefit pilars
Ask scientific questions sooner
Replace repeated preparation with reusable ingestion, harmonization and curation workflows so analysts can spend more time exploring the data.
Use more of the data you already own
Make generated, acquired and licensed data easier to discover, combine and analyze instead of leaving valuable sources isolated or underused.
Reduce repetitive manual work
Apply automation to high-volume processing and metadata tasks while retaining expert review where scientific judgment is needed.
Create consistency across programs
Give teams a shared, documented product with aligned metadata, quality criteria, provenance and access patterns.
Give AI agents governed access to internal data
Use an internal data MCP to make curated proprietary context available to agent-enabled workflows without treating raw source connectivity as data readiness.
Extend the foundation as needs change
Add new studies, modalities or analytical methods through repeatable pipelines rather than restarting the integration effort.
Proven across complex AI-ready data products
Your data, made consumable in the environment you already use
Ardigen combines life-sciences data engineering, modality expertise and AI-assisted curation to build data products around the client’s science. We complement the cloud, lakehouse and platforms already in place by doing the work required to make the data inside them usable.
Built around your questions
The target product follows the analyses and decisions it must support, rather than the boundaries of a fixed corpus or product schema.
Internal and external data in one product
We bring proprietary data into the same scientific context as the public and third-party sources needed to interpret it.
Depth across biomedical modalities
Our teams work across omics, imaging, pathology, clinical data and emerging modalities that often require specialist handling.
AI-assisted, expert-governed execution
Automation helps scale ingestion and harmonization; expert checkpoints preserve control over scientific interpretation and output quality.
MCP-first integration for private data
We can build an MCP server for internal omics data and other biomedical sources on top of the harmonized product in the client’s environment, so agents work with structured organizational context and the organization retains control of the resulting asset.
Frequently Asked Questions
What is an AI-ready data product?
It is an organised, documented and queryable body of data prepared for defined scientific and computational uses. Depending on the engagement, it may include harmonized datasets, metadata and ontologies, reusable processing pipelines, QC outputs, semantic views, notebooks, APIs or interfaces for AI-enabled workflows.
How is this different from AI-ready data architecture?
AI-ready data architecture defines the target environment, governance model and implementation roadmap. Multimodal data productization performs the data-level work: sourcing, ingesting, processing, harmonizing and curating the data that scientists need to use. Some organizations need both; others already have an environment and need help making data usable within it.
Do we need to adopt a new platform?
Not necessarily. Ardigen is platform-agnostic and can deliver within the agreed cloud, data-lake, research or analytical environment. If the current architecture cannot support the required data product, the architecture work can be scoped separately.
Can you combine our proprietary data with public or licensed data?
Yes, when access rights and the project scope permit. The product can combine internal sources with relevant public repositories, publications and third-party data while preserving provenance and the controls required by the client.
Which data types can you work with?
The supplied service scope includes single-cell and spatial omics, transcriptomics, proteomics, perturbation data, CRISPR and optical pooled screens, DRUG-seq, high-content imaging, molecular pathology and clinical records. The precise workflow depends on the source data and the scientific use case.
Does the service include scientific analysis?
The primary offer covers the data product required for analysis. An engagement may also include reusable analysis environments, standard workflows or selected downstream analyses when they are part of the agreed use case. Broader exploratory analysis and modeling should be scoped separately.
Can the data product be kept current?
Yes, where ongoing operation is included. Versioned pipelines and monitoring workflows can be designed to identify new source releases, incorporate approved updates and re-run affected processing steps through a controlled process.
Can we begin with one modality or program?
Yes. A focused starting point can establish the data model, processing workflow, quality criteria and delivery pattern before the product is extended to additional studies, programs or modalities.
How do you handle regulated or identifiable data?
Data handling, access, storage, lineage and review requirements are defined with the client for the specific environment and use case. Any GxP, HIPAA or other regulatory claim must be confirmed against the agreed delivery controls and responsibilities.
Start with one scientific question, modality or data bottleneck
See the impact
Case studies
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Make your data ready for the questions that matter
Bring us the sources, modality or workflow that is creating friction. We’ll help define the data product needed to make it usable within your scientific and technology environment.
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