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JB Consultancy

Capabilities

Deep bench across the enterprise stack

Four practice areas, so a legacy modernisation programme and a greenfield product can be staffed from the same team — with people who have run these platforms in production rather than only certified on them.

  • Enterprise Applications

    SAP

    S/4HANA implementation and migration, deep ABAP customisation, and module integration across the SAP estate.

    Core specialisations in Enterprise Applications

    • S/4HANA implementation
    • ABAP customisation
    • Module integration
    • SAP cloud migration
  • Database & Cloud Solutions

    Oracle

    Database architecture and tuning, cloud infrastructure optimisation, and the middleware that ties enterprise systems together.

    Core specialisations in Database & Cloud Solutions

    • Database architecture
    • Cloud infrastructure optimisation
    • PL/SQL
    • Middleware solutions
  • Full Stack Engineering

    Product engineering across the modern stack, built as maintainable services with cloud-native operations from day one.

    Core specialisations in Full Stack Engineering

    • Node.js
    • React
    • Angular
    • Python
    • Java
    • Go
    • Microservices
    • REST APIs
    • Cloud-native DevOps
  • AI & Next-Gen Capabilities

    Applied machine learning and generative AI, integrated into products and supported by automated data infrastructure.

    Core specialisations in AI & Next-Gen Capabilities

    • Machine learning models
    • Generative AI integration
    • Natural language processing
    • Automated data pipelines

How we apply it

Platform depth, product pace

Enterprise platforms and modern product engineering are usually bought from different firms. Holding both is what lets us integrate them without a systems-integration project in the middle.

Modernisation without a freeze

SAP and Oracle estates carry the business while the new stack is being built. We sequence work so the core keeps running — incremental migration, integration layers and parallel-run validation rather than a big-bang cutover.

AI on top of real data

Most AI features fail on data plumbing, not modelling. Because we already work inside the systems where the data lives, we can build the pipelines and the feature on top of them as one piece of work.

Need a specific skill on the ground?

Tell us the platform, the seniority and the timeline. We will come back on availability rather than on capability in the abstract.