Silent defects are strategic defects
If two plants calculate margin differently, board-level capital allocation can be wrong even when reports look complete.
CloseLoop
CloseLoop
Migration Integrity Platform
Early Access - Design Partner Cohort
Your ERP migration passed go-live. Your data didn't.
Automated validation for heterogeneous-to-SAP migrations.
Connect source and target environments, surface cross-plant discrepancies early, and track remediation before reporting confidence breaks. Full platform capabilities are available to design partners.
Free diagnostic scope: one source system + one key data domain. Target turnaround: 10 business days after receiving extracts. No production-system write access required.
Validation is the missing phase gate
Most programs track migration tasks. Fewer validate comparability, completeness, and methodology.
Design partner diagnostic available
See a sample discrepancy report before your next governance checkpoint.
What Breaks In Real Programs
The migration risks your rollout plan usually underestimates
Contrarian Truth
SAP's migration tooling does not solve heterogeneous-source validation
The hardest risk is not SAP-to-SAP movement. It is the Oracle instance from one acquisition, the custom plant system from another, and spreadsheet logic still running reporting in parallel.
Board-Level Risk
Your consolidated report can be mathematically wrong while still looking clean
If plants calculate cost and margin with different methodologies, your roll-up is an illusion of comparability.
Operational Scar Tissue
Defects ignored at go-live become permanent operating behavior
Teams normalize workarounds for years when nobody closes the validation loop after launch.
Deadline Pressure
ECC end-of-life timelines compress decisions, not risk
2027 urgency can force rushed cutovers. Validation discipline is what keeps urgency from turning into long-term defects.
Live shared scenario
24 plants across 3 source systems
Cadence: Phase Gates | Detection strictness: 95%
Any changes in the demo below update this scenario and all downstream AI + savings outputs.
Current projection
Readiness 69/100
High-severity clusters: 1 | Value at stake: $870,952
Interactive Mini Diagnostic
Simulate what your discrepancy report can expose in minutes
1. Select source stack
2. Set plant scope
3. Pick validation cadence
Cadence impact
Validation at test, parallel run, and pre/post go-live checkpoints.
Relative to Phase Gates baseline: +0% projected unresolved risk pressure, with +0 projected high-severity clusters.
Cadence directly affects pressure index, severity mix, and readiness score.
High-severity flags projected: 1 | Readiness score: 69/100
Sample discrepancy report
Click a discrepancy to inspect why it matters and how to remediate.
Discrepancy drill-down
Cross-Plant Methodology
Why it matters: Board-level profitability comparisons can be directionally wrong even when all entities appear reconciled.
Likely root cause: Different allocation formulas and local costing assumptions were migrated without methodology harmonization.
Recommended owner: Plant Controller + Global Finance Process Owner | Estimated effort: 2-4 weeks
Remediation steps
- Isolate plants with outlier margin behavior by product family and period.
- Document and reconcile costing formula differences with finance controllers.
- Publish a unified costing rulebook and map it to SAP calculation logic.
- Re-run parallel validation and freeze go-live sign-off until variance thresholds are met.
AI Copilot Demo
See how AI removes manual mapping and status-update busywork
Typical workflow: teams reconcile field mappings in spreadsheets, then manually rewrite governance updates. This demo shows what AI automates versus what still requires controller sign-off.
Using shared scenario
24 plants | 3 source systems | Phase Gates | Readiness 69/100 | Annual value at stake $870,952
Without copilot
Analysts map legacy fields one-by-one, escalate uncertainty by email, and craft PMO updates from scratch.
With copilot
AI proposes mappings, flags low-confidence items for expert review, and drafts audience-specific narratives from the same discrepancy context.
AI workflow impact (demo model)
3.0 hours saved per validation cycle
Estimated 65% reduction in mapping triage and PMO status-draft effort for the current discrepancy set.
High-focus queue
8 mappings need controller review
Copilot narrows attention to low-confidence matches so teams spend expert review time where risk is concentrated.
1. Paste legacy field names
Local heuristic mapping runs in-browser. Live OpenAI mode sends this simulated field list plus active scenario context to your server-side API route for generation.
2. AI-suggested mapping output
Selected mapping rationale
Matched semantic tokens: plant. Confidence adjusted for naming and ordering similarity.
Workflow impact: High risk: finance + data steward review required
Target schema reference
Plant, CostCenter, StandardUnitCost, G_L_Account, AssetMasterId, VendorPaymentTerms, CurrencyCode, MaterialNumber, PostingDate
3. Generate persuasive AI stakeholder narrative
Live generation uses your current scenario and findings to produce audience-specific messaging and action language.
Value Framework
Five validation pillars built for multi-plant migrations
Cross-Plant Data Integrity
Compare schema mappings, value ranges, and calculation conventions across all feeding systems before numbers hit executive reporting.
Before
Controllers manually reconcile incompatible structures across plants, leaning on tribal knowledge.
After
Automated consistency checks expose methodology mismatches and produce confidence scoring for consolidated reporting.
Post-Migration Defect Prevention
Validation at every gate catches defects during testing and parallel runs, when fixes are still cheap and auditable.
Before
Issues appear months or years later and harden into accepted workarounds.
After
Prioritized discrepancy reports and remediation tracking keep critical defects visible until resolved.
Migration Project Visibility
Give PMOs objective status by location and function instead of self-reported completion claims.
Before
Central teams cannot verify what has actually been validated across sites.
After
A single dashboard shows pass rates, open severity, and location-level readiness trends.
Heterogeneous-Source Intelligence
Use NLP-assisted field crosswalks for legacy systems where naming conventions and data structures diverge from SAP.
Before
Field-by-field mapping consumes weeks and often breaks under institutional knowledge gaps.
After
Confidence-scored mapping suggestions reduce manual effort and isolate uncertain pairs for human review.
Audit-Ready Validation Trail
Generate evidence-grade outputs for what was checked, what failed, and what was remediated at each phase gate.
Before
Validation evidence lives in fragmented spreadsheets and email threads with weak audit defensibility.
After
Timestamped validation runs and approval history provide a defensible trail for finance leadership and auditors.
Process Flow Comparator
A/B your validation workflow and expose where bottlenecks create lag cost
Process-mining-style simulation from your active scenario. Compare baseline flow vs. CloseLoop-assisted flow across queue pressure, remediation lag, and value leakage.
Cycle-time compression
39% faster
33.6 days saved across the full discrepancy lifecycle.
Remediation lag reduction
36% lower
16.7 days removed from sign-off and remediation windows.
Queue pressure
58% less backlog
216 fewer open stage-level items requiring manual coordination.
Lag cost avoided
$31,431
Modeled annualized value leakage avoided from faster discrepancy closure and revalidation.
Without CloseLoop
86.5 days to close
374 total open queue items | $42,187 lag risk
With CloseLoop
52.9 days to close
158 total open queue items | $10,756 lag risk
Stage drill-down
Remediation
Execute fixes across plants and source systems while preserving audit trail.
Without CloseLoop
24.3 days | 103 queue | $19,297 lag cost
With CloseLoop
15.7 days | 48 queue | $5,275 lag cost
Automation lever: Playbook-guided remediation tasks tied to stage-level evidence.
Built For
Authority grounded in operational reality, not launch theater
- Built for 50+ location migrations where plants carry distinct legacy systems and local calculation conventions.
- Designed around lessons from high-cost consolidation failures where closing validation was skipped.
- Purpose-built for heterogeneous-to-SAP environments, not SAP-to-SAP assumptions.
- Informed by direct conversations with plant controllers, finance leaders, and migration PM stakeholders.
No public logos at pre-launch. Design-partner references are shared privately during qualified discovery. Current intake focus is on 2026-2027 cutover programs.
“We hit go-live, but margin still needed manual normalization plant-by-plant every month-end.”
Plant Controller (anonymized, paraphrased discovery interview)
“Our PMO dashboard said green, but data comparability was still red and nobody had a shared defect view.”
Migration PMO Lead (anonymized, paraphrased discovery interview)
What we're seeing in the field
Heterogeneous-source pain is still under-scoped
Most teams scope technical migration mechanics first and discover comparability risk too late.
The cost curve is nonlinear
A defect caught pre-go-live is a task. The same defect found years later is an organizational program.
PMO visibility gaps are systemic
Distributed ownership means status often reflects social reporting, not verified validation outcomes.
Trust & Security
What we can verify today, and how we secure design-partner rollout
Evidence available now
- Interactive scenario-to-output traceability (diagnostic, AI workflow, process flow, and savings all linked).
- Anonymized operator voice from migration controllers and PMO stakeholders.
- No fabricated logos, testimonials, certifications, or benchmark claims.
Security posture
- Deployment options: customer-hosted (on-prem/VPC) or managed SaaS with data-residency controls.
- Data minimization: snapshot-first operation or sandbox connectivity, with no production write access required.
- PII handling: structural and financial validation data only; identifiable fields can be masked or hashed.
- Access controls: SSO/SAML readiness, role-based access, and immutable validation audit logs.
Operating guardrails
- Snapshot-first diagnostic mode with no production write access required.
- Controller sign-off workflow is explicit for low-confidence mapping and discrepancy closure.
- Cross-plant discrepancy ownership and remediation accountability are surfaced by stage.
- Compliance roadmap transparency: SOC 2-ready controls in place; certification is planned post design-partner phase.
Trust packet for qualified teams
- Architecture and data-flow walkthrough for technical due diligence.
- Sample discrepancy evidence pack and remediation ownership format.
- Design-partner operating cadence, scope boundaries, and onboarding checklist.
- Diagnostic qualification criteria: active S/4 program, 5+ plants, and extract-access readiness.
Savings Simulator
Play with your own assumptions and pressure-test migration economics
24 plants
3 source environments
14 hours
$98 / hour
18 months
Projected annual value at stake
$870,952
$72,579 potential value preserved per month.
Annual manual validation cost
$395,136
Annual validated operating cost
$150,152
Potential late-defect exposure
$1,490,400
Modeled labor savings
$244,984
Modeled risk avoidance value
$625,968
Demo assumption model. Customer benchmarks replace these baselines during discovery.
Early Access
Request a migration diagnostic
Free assessment scope: one source system + one key data domain (material master, asset register, or cost data). Designed for active S/4 migration programs.
Your current simulation assumptions are attached: 24 plants, 3 source systems, readiness 69/100.
- Qualification focus: active S/4 program, 5+ plants, and extract-access readiness.
- Deliverable: standardized discrepancy report with remediation guidance.
- Target SLA: 10 business days after validated extracts are received.