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# SELECTED-WORK

Work that's running.

Real projects in production — specific tools, real messes, what we actually did. Clients are anonymized under NDA; problems, technical approach, and measured outcomes are reported as-is. References available on request.

  1. // automotive

    12 weeks

    A leading automotive OEM

    predictive analytics for production quality

    Defect detection drowning in noise. Predictive model cut false positives 40% and sped up triage 2.5×. Plant managers stopped chasing ghosts.

    fewer false positives
    40%
    faster triage
    2.5×
    • Power BI
    • Azure
    • Python
    • scikit-learn

    → read case study

  2. // fmcg

    8 weeks

    A major European FMCG brand

    real-time sales reporting for field teams

    Replaced a 2-day Excel cycle with same-day Power BI dashboards for 40+ field managers. Self-serve, real-time, no tickets.

    faster reporting
    3×
    field managers self-serve
    40+
    • Power BI
    • BigQuery
    • GTM
    • dbt

    → read case study

  3. // healthcare

    16 weeks

    A healthcare provider

    unified customer data platform

    4 CRMs and 3 billing platforms unified into one source of truth. 87% of duplicates resolved. Privacy-by-design with full GDPR audit trails.

    duplicates resolved
    87%
    systems unified
    7
    • GCP
    • dbt
    • Python
    • OneTrust

    → read case study

What these projects have in common.

Different sectors, same failure mode: data that exists but can't be trusted or reached in time — a quality team drowning in false alerts, field managers steering on two-day-old spreadsheets, patient records scattered across seven systems. In each case the fix wasn't a bigger tool. It was architecture: one trusted data layer, automated pipelines, and outputs delivered inside the tools people already use.

Every engagement here ran with the same two people who answer your first call — senior delivery, EU-hosted infrastructure, GDPR by design, and a handover that leaves the internal team owning the system. That's also why each case study lists the parallel-run and adoption details, not just the headline numbers.

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