Most automation projects fail because they automate a broken process. Process mining uses event data from your existing systems to show you exactly how your processes actually run — not how they're supposed to run.
RaftLabs uses process mining to find where work gets stuck, where exceptions pile up, and which steps are costing the most before we automate anything. Discovery before build.
Event log extraction from your ERP, CRM, helpdesk, or BPM system
Process maps showing actual flow, cycle times, and bottleneck frequency
Variant analysis to find which process paths cause 80% of delays
Prioritised automation recommendations grounded in your real process data
Recent outcomes
Voice AI · Research
Text-based interviews converted to automated phone calls
6× deeper insightsAI Automation · Ops
Manual invoice OCR across 40+ gas stations
20k+ txns day oneLoyalty · Retail
SuperValu & Centra loyalty platform with receipt validation
1,062 users in 4 weeksSaaS · Logistics
Multi-carrier shipping hub for Indonesian eCommerce
2,000+ shipments yr 1Recognition
Spent money automating a process and found out it works differently in practice than it did on paper?
Trying to decide which processes to automate but working from assumption and gut feel rather than data?
In short
RaftLabs provides process mining services that extract event data from ERP, CRM, helpdesk, and BPM systems to map how business processes actually execute. We identify bottlenecks, exception paths, and cycle time drivers using tools including Celonis, ProM, and custom Python-based process analytics pipelines. A focused process mining engagement covering one end-to-end process typically takes 3 to 5 weeks and costs $12,000 to $30,000. Output is a process map, variant analysis, and a ranked list of automation opportunities with estimated time savings.
Trusted by
Most automation projects are designed from a whiteboard diagram that shows how the process should work. They get built, and then the team discovers the actual process has 12 variants, 3 exception paths, and a workaround that 40% of cases use instead of the official flow.
Process mining reads your system's event logs and shows you what's actually happening. Every case. Every variant. Every step that should take 2 hours but consistently takes 2 days. Before we automate anything, we show you what you're actually automating.
Capabilities
Extraction of event data from your operational systems — ERP (SAP, Oracle, Dynamics), CRM (Salesforce, HubSpot), helpdesk (ServiceNow, Zendesk, Jira), and custom databases. Each event record contains a case ID, an activity name, a timestamp, and optional attributes (resource, cost, outcome). For systems without native event logs, we build lightweight instrumentation at the API or database level. Raw event data is validated and preprocessed before analysis — missing events and timestamp anomalies identified and resolved in collaboration with your team.
Automated process discovery from event logs using conformance-aware algorithms (Inductive Miner, Heuristics Miner, Directly-Follows Graph) that reconstruct the actual process flow, not a hand-drawn approximation. The discovered process map shows: the frequency of each path (how many cases followed each route), the median and 95th percentile cycle time per step, and the resources involved at each stage. Interactive visualisation lets you filter by time period, case attribute, or process variant to drill into specific segments. Swimlane views for cross-team handoffs where process delays concentrate at boundaries.
Variant analysis ranks every distinct process path by frequency and cycle time. Most processes have one intended path and many actual paths — variant analysis shows which deviations are intentional design decisions vs. exceptions that accumulate delay. Bottleneck identification finds where work waits: which steps have the largest gap between start time and completion time, which activities produce the most rework, and which handoffs generate the most follow-up. Root cause analysis drills into bottleneck cases to identify whether the delay is caused by resource availability, system latency, missing data, or an upstream exception that propagates through the rest of the process.
Comparison of your actual process against a defined reference model to identify deviations: steps taken out of sequence, mandatory approvals skipped, required checks bypassed in specific cases. Conformance checking produces a quantified deviation report: which percentage of cases are fully conformant, which deviations occur most frequently, and which specific cases deviated on which specific steps. Directly applicable to audit and compliance requirements — SOX for finance, HIPAA for healthcare data handling, GDPR for data subject request processing. The output is an evidence-ready deviation log, not just a diagram.
Translation of process mining findings into prioritised automation opportunities with supporting evidence. Each opportunity scored on: annual time cost (minutes per case × case volume × cost per minute), automation feasibility (structured inputs, rule-based logic, no required human judgement), implementation complexity (number of systems involved, data quality, exception frequency), and expected net improvement (cycle time reduction, error rate reduction, headcount freed). Output is a ranked shortlist of automation targets your team can brief against — not a generic list of "things that could be automated" but a case for each specific one grounded in your data.
Live process dashboards connected to your operational data via Celonis or a custom pipeline feeding your existing BI tool (Power BI, Looker, Tableau). Key metrics tracked continuously: process conformance rate, average cycle time per variant, exception rate by step, and SLA adherence. Anomaly alerts when a metric crosses a defined threshold — a sudden drop in conformance rate or a spike in cycle time signals a process or system change worth investigating before it becomes a customer-visible problem. Process improvement tracking shows whether automation or process redesign interventions are delivering the expected cycle time reductions.
Tell us which process you want to understand, what systems it runs through, and what you're trying to decide. We'll scope a process mining engagement that answers it.
How it works
We identify which systems contain the event data for your target process, extract the raw event logs, and validate data completeness. The scoping phase also defines the case boundaries — what constitutes the start and end of one process instance — and the time period for analysis.
Automated discovery algorithms reconstruct the actual process flow from event sequences. We generate process maps, variant lists, and cycle time distributions. Your team reviews the discovered process to confirm the interpretation — catching domain-specific edge cases the algorithm can't contextualise alone.
We drill into the variants and steps with the highest time cost and exception rate. For each bottleneck: the data showing where the delay concentrates, the case-level examples of the worst instances, and the root cause hypothesis confirmed against the raw event data. No guesswork.
Findings are translated into a prioritised automation roadmap: each opportunity with its time savings estimate, feasibility assessment, and recommended approach. The handoff document is directly briefable to an automation or development team — including us, if you want to move straight from discovery to build.
Business Process Automation
Automate the processes process mining identifies as the highest-value targets.
RPA Services
Robotic process automation for processes that run through desktop and legacy systems.
Workflow Automation
Structured workflow automation for multi-step approval and routing processes.
Business Intelligence
Dashboards and reporting built on the same operational data your process mining runs on.
Data Engineering
Data pipelines that keep your event logs clean, consistent, and ready for ongoing analysis.
Process mining extracts event logs from IT systems — ERP, CRM, helpdesk, BPM platforms — and reconstructs the actual sequence of steps taken in a business process. Unlike process mapping workshops (which capture the intended process), process mining shows you what actually happened: every variant, every exception, every case that took 3 days instead of 3 hours, and exactly where it got stuck. The output is a data-driven process map rather than a whiteboard diagram.
We extract event logs from SAP, Oracle, Microsoft Dynamics, Salesforce, ServiceNow, Jira, Zendesk, Freshdesk, HubSpot, custom databases, and any system with a transaction log or audit trail. Most ERP and CRM systems have built-in event tables (SAP CDHDR/CDPOS, Oracle WF_ITEM_ACTIVITY_STATUSES). For systems without native event logs, we build lightweight instrumentation at the API or database layer to capture the events we need. Minimum requirements: a case ID, an activity name, and a timestamp per event.
We use Celonis for commercial process mining engagements where the client wants an ongoing live dashboard connected to production data. For project-based engagements, we use PM4Py (open-source Python library) and custom Jupyter-based analysis pipelines — which give us more flexibility for custom metrics and are more cost-effective for a one-time discovery engagement. We also use ProM for academic-grade conformance checking. Tool selection is based on your needs: ongoing monitoring vs. one-time discovery, and whether you want a standalone dashboard or findings integrated into your existing BI tooling.
Process mapping is a workshop exercise where you ask people how the process works and draw what they tell you. It captures the intended process and is useful for documentation and alignment. Process mining is data-driven: it reads the actual transaction logs from your systems and reconstructs what happened case by case. The two often disagree significantly — the mapped process shows the happy path; the mined process shows that 40% of cases take a completely different route, usually because of a workaround your team developed 3 years ago and forgot to document.
A focused process mining engagement covering one end-to-end process (order-to-cash, procure-to-pay, incident-to-resolution) typically runs $12,000--$30,000 and takes 3--5 weeks. A broader discovery engagement covering multiple processes to identify the highest-value automation targets typically runs $25,000--$60,000 over 6--10 weeks. Cost depends on the number of systems requiring event log extraction, the volume and quality of event data, and the depth of variant and conformance analysis required.
Process mining produces a ranked list of automation opportunities with supporting data: which variants to target, which bottlenecks to remove, and which exceptions to route differently. From there, the findings feed directly into an automation or RPA project scoped around the actual process reality rather than an assumed one. RaftLabs handles both — process mining to find what to fix, and automation or workflow development to fix it. See our business process automation and RPA services for what comes next.
Yes. Conformance checking compares your actual process against the defined process model and flags deviations: steps taken out of sequence, required approvals skipped, controls bypassed in specific cases. This is directly applicable to SOX compliance in finance (are every journal entry posting and approval happening in the required order), to GDPR compliance in customer data handling (are data subject requests being processed within the required window), and to internal audit requirements in procurement (are purchase orders being approved before invoices are processed). The output is a list of specific cases where the process deviated from the required sequence, with the case ID, the deviation, and the frequency.
Work with us
We scope Process Mining Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.