Process Mining & Process Improvement
See how your processes really run. Then fix the parts that slow them down.
Order-to-cash, procure-to-pay, ticket resolution — we rebuild them from the event logs of your ERP, CRM and ticketing systems, measure bottlenecks, rework and variants, then remove them with automation and AI agents.
PROCESS MINING
Your systems already know where the time goes.
We read the event logs, draw the real process and fix what the data shows — then measure again.
Every order, purchase request, invoice and support ticket leaves a trail in your ERP, CRM or helpdesk: who did what, and when. Process mining turns that trail into a map of how work actually flows, not how the procedure says it should. The map usually holds surprises: orders that are edited five times before they ship, invoices that wait days for one approval, tickets that bounce between teams, and dozens of variants of a process everyone believes is standard.
We do the whole loop: extract the logs, clean and connect them, analyse the process on the full data set, then change the process — with integrations, automation and AI agents where they make sense — and measure the result with the same pipeline. Our team has been building business software and integrations since 2013, so the fix does not stop at a slide with recommendations.
See the real process
We reconstruct order-to-cash, procure-to-pay or ticket resolution from timestamps, not interviews: every path, how often it happens and how long it takes.
Find the leaks
Bottlenecks, waiting time between steps, rework loops, manual touches and deviations from the intended flow, ranked by how many cases and how much time they cost.
Fix and measure
We change the process in the systems themselves — integrations, rules, automation, AI agents — and compare the same metrics before and after.- Event log extraction from ERP, CRM and ticketing systems
- Data quality report: missing timestamps, duplicates, broken case IDs
- Process maps of the real flow, with frequencies and durations
- Variant analysis: the standard path and every detour
- Bottleneck and waiting-time analysis per step, team and system
- Rework loops and manual touches, counted and costed in time
- Conformance check against the intended process
- A ranked improvement backlog with expected effect
- Implementation: integrations, automation and AI agents
- Before/after dashboards from the same data pipeline
HOW WE WORK
From raw logs to a measurably better process
Each step ends with something you can check: data, a map, a backlog, a change, a number.
Pick the process and the question
One process, one business question: why do orders take this long to invoice, why do tickets get reopened. We agree on the case definition and the metrics that matter.
Extract and clean the event log
We pull activities and timestamps from the source tables, change logs and APIs, join them across systems and fix time zones, duplicates and broken links between documents.
Discover and analyse
Process maps, variants, waiting times, rework and conformance on the full data set. We review the findings with the people who run the process, because the data shows what happens and they know why.
Prioritise the fixes
A backlog ranked by impact and effort: a rule change in the ERP, a missing integration, an approval that can be automated, a step an AI agent can prepare for a person to confirm.
Implement
We build the changes ourselves: integrations between systems, workflow automation and AI agents with clear limits, logging and a human in the loop where the decision is sensitive.
Measure before and after
The same pipeline runs on new data and shows the effect on the agreed metrics. It can keep running as monitoring, so drift back to old habits is visible early.DATA, TOOLS AND RISKS
Large data sets, your tools or ours
Most of the work in process mining is data engineering. The activities you need are spread across order tables, document flows, status histories and audit logs, often in more than one system. We build extraction pipelines that run on a schedule, connect related documents (order, delivery, invoice, payment) into one case and load the result into PostgreSQL or a columnar store sized for tens of millions of events. This is the same work we do on our data engineering and analytics projects, and the same connectors we build for ERP integration.
For the analysis we can work inside the process mining platform you already have — for example Celonis — or use open-source tools such as PM4Py together with SQL and Python notebooks. The output is the same: maps, variants, durations and conformance results you can reproduce, not a one-off picture.
The risks we handle on purpose: incomplete logs that hide steps done by email or phone, timestamps that record when data was typed rather than when work happened, personal data in fields nobody needs, and conclusions drawn from a few unusual cases. We document each assumption next to the result, pseudonymise identifiers early and can keep the data on servers in Romania, on your infrastructure or on our own cloud.
Engagements can start with a fixed-scope analysis of one process and continue with implementation; for some projects a value-based model tied to the measured improvement fits better. See our AI engagement models.
FAQ
Questions we hear about process mining
What data do we need to start?
An event log: for every case (an order, an invoice, a ticket) a list of activities with a timestamp. Most ERP, CRM and ticketing systems already store this in tables, change logs or audit trails. We find it, extract it and check its quality before any analysis.
Do we need a process mining platform such as Celonis?
No. If you already use a commercial platform, for example Celonis, we work inside it and feed it clean data. If you do not, we use open-source tools like PM4Py on top of our own data pipelines, so there is no new licence to buy just to get answers.
How large can the data set be?
Tens of millions of events are normal. We load the logs into a proper analytical store, filter and sample where it makes sense and run the heavy computations on servers sized for it, so the analysis stays fast and repeatable.
What happens to personal data in the logs?
Process mining needs case IDs, activities and timestamps, not names. We pseudonymise user and customer identifiers at extraction, keep only the fields the analysis needs and can run the whole pipeline on infrastructure in Romania, on your servers or on our own cloud.
How do you prove that the changes worked?
We agree on the metrics before we change anything: throughput time, rework rate, share of cases on the standard path, manual touches per case. The same pipeline that produced the baseline measures the process again after the change, so the before and after are directly comparable.
Have a process that feels slow? Let the data show why.
Tell us which process and which systems it runs through, and we will tell you what data we need and what a first analysis would look like.