EXPERTISE

Workflow & Automation Engineering

Redesigning the workflow before automating it, then orchestrating people, software, data and agents with the requirements of a production system.

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Capabilities

Capabilities

From business context and decisions through to production: understand, architect, build, secure, industrialize and evolve without artificially separating information systems, software, data, automation and AI.

  1. Workflow & Decision Redesign

  2. Mapping and Process Intelligence/Mining where logs allow it

  3. Decision Contracts

  4. Human × Software × Agent allocation

  5. Integrations/APIs

  6. Rules

  7. Human-in-the-loop

  8. Exception design

  9. Runtime

  10. Retries/idempotency

  11. Observability

  12. Workflow registry and governance

Engineering approach

Designing what must remain under control.

Code, rules, Data or AI: technical choices serve the system’s needs.

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Engineering approach

Low-code accelerates some workflows; code remains preferable for complex, extensively tested, high-performance or specialised components. The architecture must combine both while preserving governance and observability.

OUR ENGINEERING

  1. Problem first

    Decisions, processes, users, data, constraints and responsibilities before tools.

  2. Production by design

    Security, performance, availability, scalability, CI/CD, observability, rebuilding and maintainability shape the architecture from the outset.

  3. Evidence before promise

    Tests, metrics, traces, acceptance criteria, Evaluation, Verification and regression testing provide evidence of quality.

Technologies

Technologies

  • Airflow
  • BPMN
  • Camunda
  • GraphQL
  • Kafka
  • n8n
  • Prefect
  • Redis
  • REST API
  • Temporal
  • Webhooks

Frequently asked questions

Frequently asked questions

Low-code or custom development?

Both. We use the level of abstraction suited to each component.

LET’S TALK

Let’s discuss your challenge.

A system to build or evolve?

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