From a research question to useful insight

Researchers often need a better way to ask questions of existing data, rather than another file to download. We bring data infrastructure, access permissions and visualisation together to make information easier to use. The aim is less repeated preparation and reporting, and more time for the research itself.

Build a foundation that understands your data

We start with sources, relationships, definitions and the questions researchers need to answer. What does each field mean, when is it updated and who can access it? Shared definitions help dashboards and AI-assisted tools interpret the same information consistently.

Ask without coding. Explore within your permissions.

Where the data and platform are suitable, we can implement a query layer using large language models (LLMs). A researcher might ask, “Which study sites have missing follow-up records this month?” and explore approved data without searching through predefined reports.

Writing SQL or Python for every question is not necessary. The user’s identity and role-based access rules limit what can be queried, with row-level and column-level permissions where needed. Access to an AI tool does not grant access to all underlying data.

Move from an answer to a dashboard

Query results can feed tables, charts or your agreed data visualisation platform. Dashboards connected to suitable tools such as Power BI can turn study progress, data completeness and operational workload into views your teams can act on.

Scheduled refreshes and notifications reduce the need to recreate the same report by hand. We aim to make the source, calculation definition and update time behind a result visible, so it can be reviewed in context.

Build confidence beyond the model

LLM outputs can be wrong. We test example questions, expected results, access controls and responses to ambiguous requests. Important outputs need review by authorised people; these tools do not replace clinical decisions or institutional approvals. Platform choice, language support, processing location and the information a model may use are agreed with your institution before implementation.

Research and participant data remain under institutional control. Solutions are designed for institution-approved environments and models; ISDEM Research does not access, store or use these data. Development with synthetic examples and verification by your team within its own environment preserve this boundary.

Start with a focused use case, then build on it

The first step may be one source, one research question or one dashboard. We create a reviewable prototype, gather user feedback and expand in agreed stages. Handover makes integrations, access responsibilities, monitoring steps and operating guidance clear.

These are examples of applications that can be scoped, not claims about previous client deployments or features available on every platform. Data integration and automation can provide the organised data foundation these solutions depend on.

Let’s discuss