Digital Products & Engineering
Business Process Automation
We take repetitive work off your team by simplifying the process first, then automating it with clear approvals, error handling, and ownership.
The problem
People spend their days retyping data between systems, and nobody can tell where a request is stuck.
Automation requests usually arrive with a tool already chosen, which skips the harder question of why the process has its current shape. Automate a broken process and you get the same mistakes, faster.
Exceptions are the blind spot. The written process looks simple until you meet the missing document, the mismatched invoice, the approver on leave. Much of the work lives in those cases, and a workflow that ignores them stalls quietly.
Some work shouldn't be automated at all. If a task is rare, keeps changing, or depends on judgment, upkeep can easily outrun the savings.
You may recognize this
- The same data gets typed into more than one system.
- Approvals disappear inside email threads.
- An old automation stopped working and nobody noticed.
How we work
Observe the real process
We shadow the work on people's actual screens, map it in BPMN, and rank candidates by volume, repetition, and the cost of an error.
Simplify and catalog exceptions
Unneeded steps come out. Exceptions and business rules go into decision tables, and whatever resists being written down stays with a person.
Choose the lightest tool
Native features, a tool like n8n or Make, or custom background jobs: we pick the simplest that fits and check where personal data gets processed under KVKK.
Build in control
High-risk steps get approval gates. Job queues, retries, timeouts, and an audit trail keep failures visible, and every flow has a named owner.
Run in shadow mode
The automation runs beside the manual process until results match, then takes over.
What we deliver
- Current-state process map
- Prioritized automation backlog
- Exception catalog and decision tables
- Live workflows with approval screens
- Audit trail and status board
- Flow ownership record
How we measure
- Manual touches per case, the direct measure of whether work went away.
- Cycle time from request to completion, which exposes waiting hidden between steps.
- Failed runs and time to recover, which track whether the automation deserves trust.
Supplier invoice approvals
Illustrative scenario: A retail chain receives supplier invoices by email and passes them between branches for approval. The automation reads each invoice, matches it to the purchase order, and sends clean matches to accounting. Mismatches become approval tasks for a named person, and overdue ones escalate.
Common questions
Which process should we automate first?
Pick something frequent, rule-based, already digital, and costly when it goes wrong, with a clear owner. Keep it small, because early credibility decides what the team adopts next.
Will this replace people?
The aim is moving time from copying data to work that needs judgment. Bring the people running the process in early: they know the exceptions and have to trust the result.
Should we use AI for this?
It helps with classification, document reading, and drafting. Where the outcome must be exact, rules are more reliable. Any AI step needs human review and a clear answer on where personal data goes.
Before we work together, let's think together.
Requests are answered by the founder.