Your historical data is AI's most valuable untapped asset.
Turn fragmented SAP and non-SAP legacy data into a secure, AI-ready enterprise data fabric — cutting IT operating costs up to 80%, accelerating SAP S/4HANA, and retiring legacy systems with zero disruption.
Trusted by Global Leaders
Your ERP transformation is only as strong as your data strategy.
For decades, enterprise IT landscapes evolved system by system. The result: fragmented data, mounting cost, and a foundation that can't power AI.
The future is not system-centric. It's data-centric.
Applications change. Data remains valuable for decades. Separate the two — and your digital core gets lean, clean, and AI-ready.
Separate data from applications.
- Preserves business context
- Audit-proof retention
- AI-ready historical access
- Removes legacy app dependency
Intelligently separates your data.
A lean, agile digital core built for AI, automation, and future innovation.
92% of enterprises are not AI-ready.
Not from a lack of AI tools — but because the underlying data foundation is fragmented. AI is only as valuable as the data behind it.
Data trapped in silos
Systems don't share semantics; historical records remain inaccessible.
Governance gaps
Inconsistent data quality and weak governance block AI adoption.
JiVS IMP
A unified, governed, AI-ready data layer — sitting at the centre of your data landscape. It ingests historical data from every source and delivers it, through a unified model, to SAP BDC, AI platforms, and operative applications.
Historical data, instantly accessible
Without maintaining legacy systems or duplicating massive datasets, your data becomes available across the full AI ecosystem.
A unified object layer, purpose-built for AI
Instead of moving petabytes of legacy data into expensive operational platforms, JiVS intelligently separates, governs, and exposes historical data through standardised semantic models.
Native to SAP Business Data Cloud
JiVS maps historical enterprise data into SAP's Open Resource Discovery model — allowing SAP BDC to consume historical data as if it were native SAP data.
AI & LLMs, securely connected
For non-SAP ecosystems, JiVS uses Multi-Context Protocol to securely connect AI models and LLMs to external data repositories — no costly replication required.
From legacy silos to an AI-ready data fabric
JiVS IMP acts as the compliant, unified layer between operational SAP and non-SAP sources and the full range of modern AI and analytics platforms — on SAP BTP and beyond.
One platform. Six outcomes.
SAP transformation, retirement, compliance, AI-ready data, sovereignty, and sustainability — unified.
S/4HANA Transformation
Cut migration data volumes by up to 90% and accelerate timelines.
Legacy Retirement
Decommission SAP and non-SAP systems without losing historical access.
AI-Ready Data Fabric
Unified enterprise data layer for AI, analytics, and SAP BDC.
Audit-Proof Compliance
GDPR, CCPA, IDW PS880, ISO 27001 and 27017 — by design.
Data Sovereignty
Full control over where data lives, how it's governed, and who accesses it.
Sustainability & Cost
Lower footprint and IT costs by up to 80%.
Migrating everything into S/4HANA is not a strategy.
The lift-and-shift approach moves complexity, cost, and technical debt into your new core. The clean-core strategy leaves them behind.
The lift-and-shift trap
- Massive HANA storage costs
- Long migration timelines
- Increased complexity
- Poor data quality
- Bloated systems
- Tech debt moved to cloud
- Weak AI readiness
The clean data core strategy
- Migrate only relevant operational data
- Historize legacy data compliantly
- Preserve full business context
- Retire legacy systems securely
- Cut migration effort up to 50%
- Build a clean AI-ready foundation
AI is now orchestrating enterprise transformation.
JiVS IMP introduces AI-powered capabilities that dramatically reduce transformation effort and complexity.
Intelligent Business Object Proposer
Automatically identifies relationships, tables, and business logic required for migration.
Read moreAI-Powered Historical Data Search
Retrieve historical records using natural language — without technical system knowledge.
Read morePersonal Data Identification
AI identifies and classifies personal data across fragmented systems for GDPR compliance.
Read moreLow-Code Transformation
Transformation logic created using natural language instead of manual coding.
Read moreAutomated Data Quality
AI-assisted cleansing, deduplication, and harmonization improve migration quality.
Enterprise architecture must be sustainable and sovereign.
Your enterprise data should remain independent, governed, portable, and future-proof — regardless of ERP, cloud, or geopolitical change.
Legacy systems
- Unnecessary energy use
- Operational complexity
- Larger attack surface
- Outdated infrastructure lock-in
- Significant energy reduction
- Datacenter optimization
- Legacy hardware elimination
- Lower IT carbon footprint
- Enterprise-wide sovereignty
Global enterprises are already transforming with JiVS IMP.
Decommissioned 53 legacy ERP systems (SAP, BAAN, JDEdwards, Siebel) and consolidated onto a single global SAP platform — saving over US$5 million while keeping historical data audit-proof and accessible.
Consolidated decades of historical engineering, production, and finance data into a unified, AI-ready platform — retiring legacy systems while keeping every record audit-proof and instantly accessible.
Retired 17 SAP systems and archived over 50 TB of historical data in just 12 months while improving compliance and accelerating global transformation.
Reduced SAP operating costs by 80% through selective migration and centralized historical data management.
Retired aging infrastructure while preserving 50 years of historical operational data in a compliant, searchable platform.
Centralized historical information from multiple legacy systems while reducing operational costs and improving reporting visibility.
Enterprise-proven at global scale.
Build a clean core. Unlock AI. Retire legacy complexity.
The SAP S/4HANA transformation deadline is approaching. AI initiatives are accelerating. The organizations that win the next decade will not simply migrate systems — they will modernize their data foundation.