Process analysis & target process
Capture the current state, identify bottlenecks and breaks between tools/media (e.g., as BPMN/flow). Outcome: prioritized automation candidates and a target process that’s truly viable.
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Process & Automation
We turn manual routines into reliable workflows: less copy & paste, fewer errors, shorter cycle times. From process analysis to technical implementation – with automation that runs stably, stays transparent, and can be cleanly evolved over time.
Quick Wins
Fast, tangible relief
End-to-End
Workflows across systems
Controlled
Logging, audit, monitoring
Deliver iteratively & measurably
Robust & auditable
What we automate for you
Automation is valuable when it removes friction: fewer manual steps, clear ownership, solid data quality, and transparent processes. We build automations so they work in day-to-day operations – including exceptions and error cases.
Workflow automation
Approvals, notifications, responsibilities, SLAs, and escalations – as clear workflows instead of “knowledge in someone’s head.” So processes become traceable and scalable.
RPA & system bridges
When systems don’t provide good interfaces: RPA as a pragmatic bridge. Implemented stably, with clear rules, monitoring, and a plan to later replace it with APIs.
Data flows, jobs & orchestration
Background jobs, queues, schedules, retries, and idempotency – so automations remain stable, absorb peak loads, and keep running in a controlled way even when errors occur.
Data quality & business rules
Validation, deduplication rules, plausibility checks, and clear ownership per data object. This creates reliable data – not just “automated errors.”
Monitoring & continuous optimization
Logs, metrics, alerts, and audit trails for automations. So you can measure cycle times, error rates, and impact – and continuously improve with intention.
How automation becomes sustainable instead of “fragile”
We don’t automate blindly. First we clarify goals, exception cases, and responsibilities, then we build robust flows with operational transparency. The result is a workflow that works under real conditions – and doesn’t break at the first special case.
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Discovery & process capture
Capture the current process, pain points, exceptions, and data sources. Outcome: an automation backlog with priorities and clear success criteria (e.g., time saved, error rate).
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Target process & architecture
Define the target workflow: rules, responsibilities, approvals, data contracts, and error scenarios. Then choose the right technical implementation (API, webhook, job, RPA) – stable and maintainable.
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Iterative implementation
Step-by-step automation in short cycles: quick wins first, then expansion. Including tests, logging, and clean handovers – so trust in the automation can grow.
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Operations & optimization
Monitoring, alerts, audit trails, and clearly defined responsibilities. This keeps flows stable, handles exceptions cleanly – and improves the automation continuously.
Technology & standards
DevSolux automates based on proven standards: clear process models, robust execution logic, secure permissions, and full operational transparency. What matters isn’t the tool – it’s data quality, stability, and maintainability.
- Process modeling: clear workflows, responsibilities, exception cases (e.g., BPMN/flow logic)
- Robustness: idempotency, retries, timeouts, queue/job strategies
- Security: roles & permissions, secure secrets, traceable audit logs
- Observability: structured logs, metrics, alerts & runbooks
Questions that almost always come up at the beginning
Especially suitable are recurring routines with clear rules: data capture, reconciliation, approvals,
reporting, status updates, notifications, or handoffs between systems.
Ideal are processes where time loss, errors, or breaks between tools/media are noticeable.
APIs are usually more stable and easier to maintain when they’re available.
RPA makes sense when systems don’t have suitable interfaces or when a short-term bridge is needed.
We choose the option that achieves your goals most reliably – and design RPA pragmatically so it doesn’t become “fragile.”
By deliberately designing exception and error paths: validation, clear responsibilities,
sensible retries, manual fallbacks, audit trails, and monitoring.
This keeps processes controlled – even when external systems or data get “messy.”
With clear KPIs: cycle time, manual touchpoints, error rate, ticket volume, SLA compliance, and cost per case.
We define these metrics during discovery – and make them measurable in operations via logging/metrics/reports.
A brief description of the process (who does what, with what, how often), the systems involved,
typical edge cases, and what “better” specifically means (e.g., 30% less effort).
After that, we propose a clear next step – often a short discovery to produce a reliable effort estimate.
Ready to noticeably reduce manual work?
Briefly describe your process, the systems involved, and where time is currently being lost. We’ll get back to you with a clear, actionable next step – including a quick-win assessment.
Let’s get started