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Case studies

Example engagements, clearly marked as samples

These scenarios show the shape of the work. They are not descriptions of named customers, and they do not report measured results.

  • AI Agent Build

    Operations agent for interface follow-up

    Healthcare operations (illustrative)

    Challenge. Interface analysts spend the morning checking which feeds arrived, which messages failed validation, and which partner is waiting on a reply. The checks live in several consoles and a shared inbox.

    Solution. An operations agent gathers the overnight feed status, drafts a short exception list, and proposes the next check for a person to confirm. It does not close a clinical issue or change a message on its own. The analyst remains responsible for the action.

    • AI agent
    • HL7 v2
    • FHIR
    • Cloud monitoring
    Read moreabout Operations agent for interface follow-up

  • Healthcare Data Integration

    Multi-system clinical data integration

    Healthcare provider (illustrative)

    Challenge. A care delivery organization maintains separate admission, laboratory, and scheduling feeds. Operations staff reconcile the same encounter in more than one system, and downstream reporting arrives too late to be useful during the day.

    Solution. An integration layer receives the operational feeds, validates required segments, and publishes a consistent encounter record for analytics and workflow tools. Each source keeps its system of record. The new layer documents the mapping rather than hiding it.

    • HL7 v2
    • FHIR
    • SQL
    • Azure Data Factory
    Read moreabout Multi-system clinical data integration

  • Data Pipeline Modernization

    Data pipeline modernization

    Healthcare operations (illustrative)

    Challenge. Nightly file drops and one-off scripts feed a reporting database. Failures are noticed when a dashboard is empty, and nobody can say which transformation last changed a measure.

    Solution. Scheduled pipelines replace the scripts. Each stage has a contract, a row-count check, and a record of the source file or extract it processed. Analysts keep the same warehouse tables while engineering gains a path they can rerun.

    • Python
    • SQL
    • PostgreSQL
    • Azure Data Factory
    Read moreabout Data pipeline modernization

  • FHIR Interoperability

    FHIR exchange layer for clinical systems

    Health technology (illustrative)

    Challenge. Partner applications need patient, encounter, and observation data, but the system of record exposes a private API and a set of HL7 feeds. Each new partner starts a custom project.

    Solution. A FHIR R4 facade exposes a small, documented set of resources. The facade reads from the existing store and feeds, applies the agreed profiles, and keeps authorization at the API boundary. Partners integrate once against the published contract.

    • FHIR R4
    • REST
    • JSON
    • HL7 v2
    Read moreabout FHIR exchange layer for clinical systems

  • Cloud Application Modernization

    Cloud application modernization

    Enterprise healthcare IT (illustrative)

    Challenge. An internal web application and its database run on aging servers. Releases are manual, backups are inconsistent, and the team cannot add capacity without a hardware request.

    Solution. The application moves to managed compute and a managed database on the customer cloud account. The same functions remain. Deployment, backup, and monitoring become repeatable, and the team keeps a tested rollback path.

    • Azure
    • AWS
    • PostgreSQL
    • Node.js
    Read moreabout Cloud application modernization

  • Enterprise Reporting and Analytics

    Operational reporting and analytics model

    Healthcare administration (illustrative)

    Challenge. Leaders receive spreadsheet extracts that disagree. Measure definitions live in report filters, so a small change in one workbook changes the story without a review.

    Solution. A curated reporting model defines the grain, the measures, and the filters in the warehouse. Power BI reads that model. Analysts can still explore, but published numbers come from the shared definitions.

    • SQL
    • Power BI
    • PostgreSQL
    • SQL Server
    Read moreabout Operational reporting and analytics model