Three engagements across Insurance, E-mobility, and Industrial — each anchored in a different plan level. The methodology from the book, applied to real operating constraints. All engagements are confidential.
A mid-size DACH insurance carrier. Claims reviewers spend 40% of their time on triage decisions that follow a deterministic pattern 72% of the time. The use case is obvious — the problem is shipping it.
The constraint isn't the model. It's the integration layer: the claims data sits in a legacy core system that doesn't expose clean APIs, and the compliance team needs an audit trail before they'll sign off on autonomous decisions. Plan 2 Accelerator — 6 weeks, one workflow, one KPI. The triage loop is rebuilt AI-native on a thin data plane that bridges the legacy core without replacing it. Shadow mode for weeks 3–5, then live in week 6.
Workflow audit. Data mapping. Integration architecture. KPI lock — triage cycle-time, measured weekly.
Thin data plane bridging the legacy core. Classification model. Audit log spec. DSGVO sign-off.
Shadow mode vs. human reviewers for 2 weeks. Cutover in week 6. Runbook + handover.
A DACH operator of 3,200 public charging points. Two load-bearing operations run on fragmented tooling: billing reconciliation (CPO-to-MSP handoffs, disputed sessions, refund routing) and field-fault triage (which faults route to a technician, which can be resolved remotely, which need firmware escalation).
These aren't independent problems. Both need the same session-level data plane to work. Plan 3 OS Build — 13 weeks — installs both workflows on a shared data architecture, adds the governance layer (EU AI Act, audit trail), and hands the ops team a console to manage exceptions without engineering support.
Shared session-level data plane. Billing reconciliation workflow live and measured by week 6.
Second workflow live. Decision architecture — how the system routes, escalates, and audits faults.
Integration layer. Operator console. EU AI Act baseline. Compound roadmap. Handoff.
A German Mittelstand manufacturer. The after-sales operation runs on a 9-year-old booking and spare-parts system. Key-person dependency risk — two people who know how it works. The AI initiative stalled at integration: the models are fine, the stack can't accept them.
This is a Modernization engagement. Not a replatform — a staged migration. Legacy support continues uninterrupted while the replacement gets built. Month 4: first AI-native workflow live on the new plane. Month 6: cutover. Months 7–9: slices 2–3 land on the same stack, each faster than the last because the data plane is already instrumented.
Workflow + stack audit. AI-assisted code archaeology. Slice plan + cost frame locked.
Take over legacy ops. Build slice 01 on the new data plane. Shadow mode against legacy.
Switch traffic. Legacy retires for slice 01. First AI-native workflow live. Cost-out starts compounding.
Slices 02–03 land on the same plane. Each is faster and cheaper than the last.
A selection of engagements delivered under Remote Native and its predecessor operations. Company names withheld by agreement.
AI-driven operational restructuring across a core business unit. Workflow automation, data pipeline redesign, and team enablement — resulting in a measured 60% cost reduction in the target operation.
AI readiness assessment across charging infrastructure operations. Full Level 3 implementation roadmap delivered — covering data architecture, workflow prioritization, governance baseline, and build sequencing.
Legacy stack modernization for a 9-year-old after-sales system. Staged migration architecture, AI-readiness audit, and operational takeover — clearing the path for AI-native workflows.
QR-code-based redirection tool for global field service management. Built and deployed for international technician operations across multiple markets.
PIM process optimization workshops. Workflow analysis, bottleneck identification, and process redesign for product information management at scale.
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