Solutions

Answering one focused research question means combining publications, trial records, omics, chemistry, protein annotations, and your own internal data. Each source uses different formats, identifiers, and naming. The same entity appears under different names, a broad disease category in one dataset is split into subtypes in another, and teams spend more time connecting information than analyzing it.

A governed data environment that brings the sources you need into one place and connects them through an ontology built around your scientific domain. Established biomedical resources are reused where they fit, local concepts are defined where they do not, and every mapping is reviewed. Layered storage keeps source data separate from analysis-ready sets, so a reviewer can trace any value back to where it came from.

A reusable foundation rather than another one-off extract, a searchable collection that does not depend on knowing each source's naming conventions, and documented transformation logic your team can audit.

Getting usable evidence out of published research means reading each publication and transferring results into extraction forms by hand, and studies describe populations and outcomes differently. After authorization the questions change: value committees and payers ask about length of stay, readmissions, complications, and cost in their own populations, not a trial population.

AI-assisted extraction into a structured schema covering population, intervention, comparator, outcomes, design, and results, with reviewers checking each record against the source text. For products in the market, cohort and outcome definitions, claims and patient-reported outcome linkage, and health economics analysis built on them, plus real-world performance monitoring where a deployed model is involved.

An evidence base that answers the next question without another manual search, evidence tables ready for synthesis or assessment, and a value dossier a purchasing committee can act on.

Every new hospital adds integration work, and that work rarely transfers to the next site. Trial systems and hospital record systems do not share a language. Much of what matters clinically sits in free-text notes that no structured query reaches.

Reusable connectors to electronic health records, laboratory and imaging systems, and payers, using FHIR and HL7. Trial data moves out in FHIR and clinical data back in CDISC CDASH. Language model pipelines extract structure from clinical notes.

Site rollouts that get faster rather than staying constant, a documented mapping specification, and a transfer log that holds up in an audit.

Physical experiments and bench tests are slow and expensive, and each one validates a narrow set of conditions. A device does not behave the way its nominal design predicts once it meets real anatomy. Computational evidence only carries weight when the model's scope, inputs, and credibility are documented.

Models scoped to one question. For medical devices, digital twins covering structural deformation, fluid dynamics, or heat transfer, with anatomy from imaging, geometry from CAD, and predictions compared against reserved physical observations. Every parameter is recorded with its origin.

Quantitative comparison across designs, materials, and patient anatomies under controlled conditions, the specific places physical testing should now focus, and a credibility record you can put in front of a reviewer.

Manufacturing parameters are coupled, quality attributes compete with each other, and the run budget is limited. Teams need experimental discipline quickly, and cannot send process IP to a public model to get it.

An evidence-graded constraint map and a sequential design of experiments that adapts as runs generate data. Every constraint tagged by source. Multi-response trade-offs settled by a scoring rule agreed before the first run.

More information per run, trade-offs resolved by rule, and artifacts that feed directly into quality-by-design and regulatory documentation.

Producing clinical and preclinical documents is slow and repetitive. Trial master file gaps surface late, usually when a submission is being assembled. Investigations start with assembling a record that lives across several systems.

Drafting support grounded in your own source data and templates. Continuous monitoring of trial documentation against a structured model, with notification when a completeness test fails. Investigation support that assembles the event record and proposes causes with evidence attached.

Faster first drafts, gaps found while the trial is running rather than at submission, and a provenance record on each AI-assisted section.

Boards, partners, and acquirers now probe how AI is used. Tools adopted ad hoc, unclear data lineage, and an AI narrative with no governance underneath read as risk at exactly the moment a company wants to look like an asset.

A structured review of where AI is already in use, what data it touches, what governance exists, and where the gaps sit against the standards your buyers and auditors will apply.

A clear statement of your current position, a ranked list of what to fix, and an AI story you can defend in a room full of people paid to find holes in it.