Capability brief · Process MiningCapability 12 of 14

See How Your Processes Actually Run - Not How the SOP Says They Should

Neume's AI process mining reconstructs real execution paths from system event logs, surfaces bottlenecks invisible to management, and quantifies the gap between documented procedures and ground-truth behavior -- in weeks, not quarters.

Book a call with an engineer How it’s built

73%

Of Processes Deviate from Documented SOPs

4.2x

Faster Bottleneck Identification vs. Manual Audits

35%

Average Cycle Time Reduction After Remediation

<3 Weeks

From Event Log Ingestion to Actionable Process Map

01Overview

AI Process Mining

What it is

AI process mining ingests event logs from ERP, CRM, ticketing, BPMS, and other transactional systems to algorithmically reconstruct how work actually flows through an organization. Instead of relying on interviews, workshops, or static Visio diagrams, the technology builds empirical process models from timestamped system data -- every case, every variant, every handoff. The result is a living, data-driven map of operational reality that exposes rework loops, unauthorized workarounds, bottleneck queues, and compliance deviations that no manual audit can detect at scale.

Why it matters

Every enterprise operates two versions of its processes: the one documented in the SOP binder and the one employees actually follow. The gap between the two is where margin leaks, compliance risk accumulates, and cycle times silently inflate. Traditional process improvement methods (Lean, Six Sigma, consulting-led workshops) rely on stakeholder self-reporting, which is subject to recall bias, political framing, and sample-size limitations. Process mining eliminates these distortions by grounding analysis in exhaustive, objective system data. For organizations preparing for AI-driven automation, process mining is the essential precursor -- you cannot automate what you do not accurately understand.

How Neume does it differently

Most process mining deployments stall at the dashboard phase: a vendor produces a pretty spaghetti diagram, the project team presents it to leadership, and nothing changes. Neume treats process mining not as a standalone analytics product but as the opening move in a diagnostic-to-action pipeline. Our analysts pair algorithmic discovery with domain-expert interpretation to produce prioritized remediation roadmaps -- ranked by EBITDA impact, implementation feasibility, and automation readiness. Every process variant we surface is tagged with a recommended next step: standardize, automate, escalate, or eliminate. This is process mining that feeds directly into operational improvement, not a visualization exercise.

02Core capabilities

What this system can do.

Automated Process Discovery

Reconstruct end-to-end process models directly from event logs without manual mapping. The algorithm identifies every execution variant -- including paths that no stakeholder mentioned in interviews -- and ranks them by frequency, duration, and cost impact.

Bottleneck Detection & Quantification

Pinpoint exactly where cases stall by analyzing inter-activity wait times, resource utilization patterns, and queue depths. Each bottleneck is quantified in hours lost and dollars at risk, so prioritization is immediate rather than debatable.

Conformance Checking

Overlay discovered process models against the intended reference model (from SOPs, regulatory requirements, or internal policies) to identify every deviation. Each non-conformance is classified by type -- skipped steps, reordered activities, unauthorized loops -- and scored by risk severity.

Root Cause Analysis

Go beyond symptom identification to isolate the upstream drivers of process dysfunction. Correlate deviations and delays with attributes like case type, geography, handler, time-of-day, and system version to surface causal patterns invisible to aggregate reporting.

Process Variant Clustering

Group the dozens or hundreds of observed execution paths into meaningful clusters based on structural similarity. Distinguish between legitimate complexity (different case types requiring different paths) and unnecessary fragmentation (the same case type handled six different ways).

Continuous Monitoring & Drift Detection

Move beyond one-time snapshots to ongoing surveillance. Detect when process behavior begins drifting from the established baseline -- whether due to staffing changes, system updates, or gradual workaround proliferation -- and alert stakeholders before the drift compounds.

03Architecture

How it’s built.

Neume's process mining architecture is a three-layer pipeline: ingest raw event data from source systems, apply discovery and analysis algorithms, and surface findings through interactive diagnostics that feed directly into the remediation planning workflow.

Event Log Ingestion & Normalization

Extract, transform, and unify event data from heterogeneous source systems into a canonical event log format (Case ID, Activity, Timestamp, Resource, Attributes). Handles schema mismatches, clock-skew correction, and multi-system case correlation.

ERP event extractors (SAP, Oracle, Epicor, NetSuite)
CRM and ticketing connectors (Salesforce, ServiceNow, Zendesk)
BPMS and workflow log adapters (Camunda, Appian, Power Automate)
Email and communication timestamp parsers
Schema normalization and case-ID stitching engine
Data quality validation and completeness scoring

Discovery & Analysis Engine

Apply process mining algorithms to the normalized event log to produce process models, variant analyses, bottleneck maps, and conformance reports. This is the computational core where raw data becomes operational insight.

Process discovery algorithms (Alpha, Heuristic, Inductive miners)
Conformance checking engine with fitness and precision metrics
Bottleneck analyzer with wait-time decomposition
Variant clustering and structural similarity scoring
Root cause correlation engine (multi-attribute regression)
Temporal drift detection and change-point analysis

Diagnostic Output & Action Layer

Translate algorithmic output into prioritized, business-contextualized findings that connect directly to Neume's remediation and automation planning workflows. This is where process mining becomes process improvement.

Interactive process map with variant overlay and filtering
Bottleneck impact dashboard (hours lost, cost, downstream effects)
Conformance deviation register with risk scoring
Automation readiness tagger (per activity and per variant)
Remediation roadmap generator with EBITDA impact ranking
Export to Neume diagnostic report and engagement planning tools

Integration approach

Neume connects to source systems via read-only database views, API exports, or flat-file log extracts -- never requiring write access or agent installation in production environments. For ERP systems, we leverage standard audit-log and change-document tables. For multi-system processes (e.g., order-to-cash spanning CRM, ERP, and logistics), we stitch cases across systems using shared identifiers (order numbers, customer IDs, ticket references). Typical integration setup completes in 5-10 business days.

04Cross-industry deployments

Process Mining in production.

Deployment 01Financial ServicesLoan Origination Cycle Time Reduction

The problem

A mid-market commercial lender documented a 12-step loan origination process with a target cycle time of 18 business days. Actual median cycle time had crept to 34 days, but no one could pinpoint where the delays concentrated. Management suspected underwriting; underwriters blamed document collection.

44% cycle time reduction; $2.1M incremental annual revenue from faster closings

How it works: Neume ingested 14 months of event data from the LOS, document management system, and email timestamps. Process discovery revealed 47 distinct execution variants where the SOP prescribed one. Bottleneck analysis showed that 62% of total wait time concentrated in two handoff points: the transition from relationship manager to credit analyst (average 6.3-day queue) and the loop between underwriting and the borrower for missing financial statements (average 3.1 iterations per case). Neither bottleneck appeared in the documented process.
Outcome: The lender restructured the RM-to-analyst handoff with parallel routing and implemented a pre-submission document completeness check. Median cycle time dropped from 34 to 19 days within one quarter.

Deployment 02HealthcarePatient Discharge Process Optimization

The problem

A regional hospital system experienced chronic discharge delays averaging 4.7 hours beyond the physician's discharge order. Bed turnover bottlenecks cascaded into ED boarding, surgical cancellations, and patient dissatisfaction scores. Internal process improvement teams had conducted three Lean kaizen events without sustained improvement.

60% reduction in discharge delay; 11% improvement in bed turnover rate

How it works: Neume extracted event logs from the EHR, pharmacy system, transport scheduling, and case management platform. Discovery revealed that discharge was not one process but five, depending on payer type and disposition. Conformance checking against the hospital's published discharge protocol showed a 68% deviation rate -- most commonly, pharmacy reconciliation occurring after transport was requested rather than before, causing a cascade of cancellations and re-requests.
Outcome: The hospital re-sequenced pharmacy reconciliation upstream and introduced parallel notification to transport and environmental services. Average discharge delay dropped from 4.7 to 1.9 hours.

Deployment 03ManufacturingOrder-to-Ship Conformance & Bottleneck Elimination

The problem

A discrete manufacturer with $180M revenue maintained a 98% on-time delivery KPI on its executive dashboard but was receiving increasing customer complaints about late shipments. The disconnect between the KPI and customer experience was unexplained.

18-point improvement in true on-time delivery; $640K reduction in expediting costs

How it works: Process mining across the ERP (order entry through shipment confirmation) revealed that the 98% metric was calculated against a revised promise date, not the original customer-requested date. Against the original request date, on-time performance was 71%. Bottleneck analysis identified the engineering review step for custom-configured orders as the primary delay driver -- 40% of orders required engineering review, and the average queue time was 5.8 days due to a single engineer handling all reviews sequentially.
Outcome: The manufacturer implemented tiered engineering review (auto-approve standard configurations, expedited review for minor modifications, full review only for novel designs) and cross-trained two additional engineers. On-time-to-original-request improved from 71% to 89% within two quarters.

Deployment 04InsuranceClaims Adjudication Rework Reduction

The problem

A P&C insurer's claims operation showed a 23% rework rate -- nearly one in four claims required re-opening after initial adjudication due to missing documentation, incorrect reserves, or policyholder disputes. Each rework cycle added an average of 11 days and $420 in handling cost.

52% rework reduction; $1.8M annual savings in handling costs

How it works: Neume mined 18 months of claims event data from the claims management system and document repository. Variant analysis revealed that claims touching three or more adjusters had a 41% rework rate versus 9% for single-adjuster claims. Root cause analysis correlated rework with mid-process reassignment events -- typically triggered by adjuster PTO or caseload rebalancing -- where the incoming adjuster lacked context on prior decisions and defaulted to re-requesting documentation the previous adjuster had already reviewed.
Outcome: The insurer implemented structured handoff notes (auto-generated from system activity) for reassigned claims and restricted mid-adjudication reassignment except for escalations. Rework rate dropped from 23% to 11%.

Deployment 05Logistics & Supply ChainFreight Invoice Audit Process Transparency

The problem

A 3PL provider processed 12,000+ freight invoices monthly with a 6-person audit team. Despite the team's effort, 8% of invoices contained overcharges that escaped detection, costing the company an estimated $1.4M annually. Management could not determine whether the problem was staffing, training, or process design.

74% reduction in overcharge escapes; $1.1M annual cost recovery

How it works: Process mining on the audit workflow (from invoice receipt through approval or dispute) revealed that auditors were processing invoices in FIFO order regardless of value or risk profile. High-value invoices received the same 4-minute average review as $200 LTL shipments. Additionally, 31% of auditor time was spent on a manual rate-lookup step that could be automated. Conformance checking showed that the prescribed three-way match (invoice vs. BOL vs. rate agreement) was skipped entirely on 19% of invoices during end-of-month surges.
Outcome: The 3PL implemented risk-based routing (high-value and high-variance invoices flagged for detailed review), automated rate lookups, and eliminated the end-of-month compliance gap with staggered processing. Overcharge escape rate dropped from 8% to 2.1%.

Deployment 06Professional ServicesProposal-to-Engagement Conversion Acceleration

The problem

A mid-size consulting firm's proposal-to-signed-engagement cycle averaged 47 days, but leadership had no visibility into where proposals stalled. Win rates had declined from 38% to 29% over two years despite no change in proposal quality scores.

51% cycle time reduction; 6-point win rate improvement worth ~$2.8M incremental revenue

How it works: Neume ingested CRM opportunity data, document management timestamps (proposal drafts, reviews, client sends), email activity logs, and contract management events. Discovery revealed that the internal legal and pricing review consumed 14 of the 47 days on average -- and that proposals requiring partner sign-off had a 9-day longer cycle than those within delegation-of-authority thresholds. Critically, proposals that exceeded 30 days total cycle time had a 16% win rate versus 41% for those completed within 20 days, confirming that speed was a competitive differentiator the firm was systematically failing to deliver.
Outcome: The firm raised delegation-of-authority thresholds for standard engagements, implemented parallel (rather than sequential) legal and pricing review, and established a 21-day cycle time target with escalation triggers at day 14. Average cycle time dropped to 23 days and win rate recovered to 35% within three quarters.

05Comparison

Why not off the shelf?

Celonis / UiPath Process Mining

Limitation

Enterprise-grade platforms with 6-12 month implementation timelines, $250K-$1M+ annual licensing, and heavy IT involvement for connector setup and data modeling. Designed for Global 2000 organizations with dedicated process excellence teams. Output is a self-service analytics platform that requires in-house expertise to operate and interpret.

Neume advantage

Neume delivers process mining as a managed diagnostic service, not a software platform. No licensing fees, no internal team required to operate the tool, no 9-month implementation. We extract the event data, run the analysis, interpret the findings, and deliver a prioritized action plan -- typically within 4-6 weeks. For mid-market organizations without a process excellence function, this is the difference between insight and shelfware.

Manual Process Mapping (Lean / Six Sigma Workshops)

Limitation

Relies on stakeholder interviews and observation, which capture at most 3-5 process variants out of the dozens or hundreds that actually exist. Subject to recall bias, political framing, and the Hawthorne effect (people perform differently when observed). A single Lean mapping exercise costs $50K-$150K in consulting fees and captures a snapshot that is outdated within months.

Neume advantage

Algorithmic discovery is exhaustive, objective, and reproducible. It surfaces every variant, every bottleneck, and every deviation -- including the ones nobody mentions in workshops because they are embarrassing, invisible, or simply unknown. And because it runs on system data, it can be refreshed continuously rather than repeated as a periodic consulting engagement.

BI Dashboards & KPI Reporting

Limitation

Traditional BI tools (Tableau, Power BI, Looker) report aggregate metrics -- average cycle time, throughput, completion rates -- but cannot reconstruct the sequence of activities that produced those metrics. They tell you that cycle time increased by 15% but cannot tell you which handoff, which rework loop, or which variant is responsible.

Neume advantage

Process mining operates at the event level, not the aggregate level. It does not just report that a KPI degraded -- it traces the exact execution paths, handoffs, and decision points that caused the degradation. This is the difference between knowing you have a problem and knowing exactly where to intervene.

Task Mining / Screen Recording Tools

Limitation

Task mining captures desktop-level user activity (clicks, keystrokes, application switches) and is useful for understanding individual task execution. However, it requires agent installation on employee workstations, raises privacy concerns, and captures only the human-facing portion of processes that span multiple systems and automated steps.

Neume advantage

Neume's approach mines system event logs rather than user screens, capturing the complete end-to-end process including automated steps, system-to-system handoffs, and batch processing that no screen recorder can see. No agent installation, no employee surveillance concerns, and full coverage of cross-system processes.

06Implementation

What deployment looks like.

01Phase 1 (Weeks 1-2)
02Phase 2 (Weeks 2-4)
03Phase 3 (Weeks 4-6)
04Phase 4 (Ongoing)

01Phase 1 (Weeks 1-2)
Source system identification, event log extraction, and data quality assessment.
02Phase 2 (Weeks 2-4)
Log normalization, process discovery, and initial variant/bottleneck analysis.
03Phase 3 (Weeks 4-6)
Conformance checking, root cause analysis, and remediation roadmap development.
04Phase 4 (Ongoing)
Continuous monitoring deployment and drift alerting. Most engagements deliver initial findings by Week 3 and a complete diagnostic by Week 6.

Prerequisites

Access to event logs or audit trails from at least one core transactional system (ERP, CRM, ticketing, claims, etc.)
Identifiable case IDs that link events belonging to the same process instance (order number, claim ID, ticket number, etc.)
Minimum 6 months of historical event data for statistically meaningful variant and bottleneck analysis
A designated process owner or operational stakeholder to validate findings and prioritize remediation
Read-only database access or scheduled log exports from source systems

Deliverables

Empirical process model with all observed execution variants, frequency, and duration statistics
Bottleneck analysis with quantified impact (hours, dollars, downstream effects) per bottleneck
Conformance report mapping every deviation from documented SOPs or regulatory requirements
Root cause analysis correlating delays and deviations with case, resource, and temporal attributes
Prioritized remediation roadmap ranked by EBITDA impact and implementation feasibility
Automation readiness assessment tagging activities suitable for algorithmic processing
Continuous monitoring configuration with drift detection thresholds and alerting rules

Human in the loop

Process mining algorithms produce models and metrics; they do not produce judgment. Neume pairs every algorithmic output with domain-expert review. Our analysts validate that discovered variants reflect genuine operational behavior (not data artifacts), contextualize bottlenecks within organizational constraints that the algorithm cannot see (union rules, regulatory holds, seasonal patterns), and ensure that remediation recommendations are operationally feasible. The algorithm finds the patterns; the human determines what they mean and what to do about them.

07Security & compliance

Engineered for trust.

Data Access

All source system connections use read-only credentials or pre-extracted log files. Neume never requires write access to client systems. Database queries are scoped to audit/event tables only -- no access to customer PII, financial records, or other sensitive data beyond what is necessary for process reconstruction.

Data Handling & Residency

Event logs are transmitted via encrypted channels (TLS 1.3) and processed in isolated, client-specific environments. Raw logs are retained only for the duration of the analysis engagement plus a contractually agreed retention period. All data is purged upon engagement completion unless ongoing monitoring is contracted.

Anonymization

Resource identifiers (employee names, user IDs) are pseudonymized during ingestion. Process mining analysis requires knowing that Activity A was performed by Resource X, but does not require knowing that Resource X is a specific named individual. Client retains the pseudonymization key.

Regulatory Alignment

Process mining outputs directly support compliance documentation requirements for SOX (control testing), HIPAA (access and workflow auditing), ISO 9001 (process conformance), and GDPR (data processing activity mapping). Conformance reports can be formatted for regulatory submission.

08FAQ

Common questions.

What if our systems don't have clean event logs?

Most systems do -- they just are not labeled as 'event logs.' ERP change documents, CRM activity histories, ticketing system status changes, email timestamps, and database audit trails all qualify. Neume's ingestion layer is designed to extract and normalize event data from systems that were never designed for process mining. In our experience, fewer than 5% of engagements are blocked by genuinely insufficient data.

How is this different from the process maps we already have?

Your existing process maps document how work is supposed to flow. Process mining reveals how it actually flows. In every engagement we have conducted, the discovered process contains variants, loops, and bottlenecks that do not appear in any documented procedure. The typical gap is not minor -- organizations routinely discover that 40-70% of cases follow paths that deviate from the documented process.

Do we need to buy a process mining software license?

No. Neume delivers process mining as a managed service. We operate the tooling, run the analysis, and deliver interpreted findings and action plans. You receive the output -- process maps, bottleneck analyses, conformance reports, remediation roadmaps -- without licensing, deploying, or staffing a software platform.

How does process mining relate to RPA and automation?

Process mining is the diagnostic step that should precede any automation investment. It identifies which activities are high-volume, rule-based, and stable enough to automate -- and equally importantly, which are too variable, exception-heavy, or poorly understood to automate safely. Automating a process you have not mined is like prescribing medication without a diagnosis.

Will this disrupt our day-to-day operations?

No. Process mining is entirely observational. It analyzes historical and ongoing event logs that your systems already generate. There is no agent installation on workstations, no workflow modification, no employee monitoring, and no impact on system performance. Your teams continue working normally while the analysis runs in the background.

How does Neume's process mining feed into broader engagement?

Process mining is the entry point of Neume's diagnostic phase. The findings directly inform which workflows are candidates for Neume's AI-augmented BPO services, which processes should be standardized before automation, and where human-in-the-loop orchestration will deliver the highest ROI. Every process mining engagement produces an automation readiness score per activity that plugs directly into Neume's implementation planning.

Where this system ships

Financial Services

High-volume, multi-step workflows (loan origination, claims, KYC/AML) generate rich event logs and suffer from undocumented process variants that inflate cycle times and compliance risk.

Healthcare

Clinical and administrative workflows (patient flow, revenue cycle, discharge) span multiple systems and stakeholders, making process visibility a persistent challenge that directly impacts patient outcomes and reimbursement.

Manufacturing & Industrials

Order-to-ship, procurement, and quality processes generate dense ERP event logs ideal for mining, and cycle time improvements translate directly to throughput and margin gains.

Insurance

Claims adjudication, policy servicing, and underwriting workflows exhibit high variant counts and rework rates that process mining can quantify and trace to root causes.

Logistics & Supply Chain

Multi-party, multi-system freight and fulfillment processes create complex event trails where bottlenecks and conformance gaps directly impact service levels and cost.

Next step

Process mining without interpretation is just a diagram. Neume combines algorithmic discovery with domain-expert analysis to turn event data into a prioritized operational improvement plan. We do not sell software licenses or leave clients with a dashboard they need to figure out -- we deliver findings, recommendations, and a clear path from diagnosis to remediation.

Neume is the only AI BPO provider that treats process mining as an integrated diagnostic phase rather than a standalone analytics product. Every process we mine feeds directly into our automation readiness assessment, workflow design, and human-in-the-loop orchestration planning. The result is a seamless pipeline from 'here is how your process actually runs' to 'here is how we will improve it' -- with quantified impact projections at every step.

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