SwiftAI helps enterprises simplify complex SAP and legacy landscapes by reducing live data volumes, preserving compliant access to history, and retiring obsolete systems with a clear, controlled plan. This includes M&A carve-out and carve-in programs, where clean, well-governed data is what makes Day-1 readiness possible. We combine transformation consulting with Agentic AI and LLMs to speed up analysis, improve governance, and make historical knowledge easier to use.
Modernization is not only about moving to a new platform. It is about deciding what should stay active, what should be retained compliantly, and what can be retired without losing business confidence.
Move only the data that still drives the business. Reduce downtime, risk, and project cost by separating active data from long-tail history.
Shrink database growth, simplify landscapes, and retire redundant infrastructure that no longer justifies its support burden.
Keep historical records available for audit, tax, legal, and operational needs while enforcing retention and deletion rules correctly.
Create a cleaner, governed data estate that makes enterprise search, RAG, and agentic automation materially more reliable.
Shorten carve-out, carve-in, and post-merger timelines with a clear view of what data and systems must separate, combine, or retire.
Reduce live-system volume without losing business access. We design retention-aware archiving strategies for SAP and adjacent enterprise platforms so production systems stay lean, auditable, and ready for future change.
Transformation programs fail when data scope stays uncontrolled. We help clients baseline volumes, classify business-critical history, improve data quality, and establish a roadmap for selective migration, retention, and retirement.
M&A timelines do not wait for a clean data landscape. We support carve-out, carve-in, and divestiture programs with rapid system separation, data splitting, and harmonization so new entities and combined organizations are operational on day one.
Retire legacy systems safely while preserving compliant, business-friendly access to historical records. We support SAP and non-SAP retirement scenarios driven by M&A, ERP consolidation, cloud migration, and technical debt reduction.
Roughly four out of five enterprise records are unstructured: contracts, invoices, correspondence, and scanned documents. We bring this content under control so it can be archived, searched, and safely retired like any other governed data asset.
AI is only useful when enterprise history is organized and trustworthy. We apply LLMs and agentic workflows to accelerate discovery, classification, reconciliation, and knowledge access across transformation programs.
We use AI where it materially improves transformation outcomes: faster system discovery, better content classification, stronger exception handling, and easier access to archived business history. The goal is not AI theater. The goal is lower effort and better control.
Use AI-assisted analysis to separate business-critical data from dormant history before S/4HANA moves.
Give finance, audit, and operations teams conversational access to archived records and documents.
Identify hidden dependencies, reporting usage, and exception paths before legacy shutdown.
Use agentic workflows to map, split, and reconcile data across legal entities during M&A separation.
Apply LLM-supported review workflows to retention exceptions, legal holds, and data-quality anomalies.
Assess system landscape, data growth, custom objects, archive readiness, retention obligations, and downstream dependencies.
Define quick wins, wave plans, business-critical history, and the right end state for each system: keep, archive, migrate, or retire.
Run archiving, historical-data preservation, access enablement, and controlled decommission activities with measurable checkpoints.
Layer on governance, AI-assisted retrieval, and ongoing data lifecycle controls so the problem does not return after go-live.