Products Deployed. Outcomes Delivered.
Three enterprise deployments. Real data. Measurable results. This is what our products look like in production.
847 Custom Code Objects Auto-Remediated. Migration Timeline Cut by 55%.
A global manufacturer used the SAP AI Upgrade Accelerator to turn a 9-month manual code remediation project into a 4-week automated sprint.
A global manufacturer with a heavily customised SAP ECC landscape faced a brownfield S/4HANA migration. Their internal estimate: 9+ months of manual custom code remediation across 847 ABAP objects before they could even begin the technical conversion. The project was stalled before it started.
We deployed the SAP AI Upgrade Accelerator to automate the code analysis, impact assessment, and remediation suggestion pipeline. The product scanned the entire custom code landscape, classified each object by risk and effort, generated remediation suggestions, and produced a prioritised migration backlog — in under 4 weeks.
847 ABAP objects scanned across FI, MM, PP, SD modules — automated risk classification and impact scoring
Automated impact assessment with AI-generated remediation suggestions and effort estimates per object
AI-generated regression test cases derived from existing business process documentation
Real-time go/no-go scoring with executive visibility across the full migration program
Automated Code Analysis
Full landscape scan with risk classification, compatibility scoring, and remediation priority ranking — in hours, not months.
AI-Generated Test Cases
Regression test cases generated automatically from existing business process documentation, cutting manual test design by 70%.
Migration Risk Dashboard
Real-time go/no-go scoring across the full migration program — giving program leadership the visibility to make confident decisions.
Custom objects auto-remediated
Across FI, MM, PP, SD
Reduction in migration timeline
vs. original estimate
Code analysis completed
vs. 9 months manual
Fewer manual test cycles
AI-generated test cases
87% Invoice Straight-Through Processing. 91% Autonomous Procurement Decisions.
A Tier-1 bank eliminated manual invoice handling with our autonomous enterprise agent framework on SAP BTP — 24/7 operation, zero human touchpoints on standard transactions.
A Tier-1 bank was processing thousands of invoices and procurement transactions daily — with a large team manually reviewing, approving, and routing each one. The process was slow, error-prone, and scaling linearly with transaction volume. Headcount was growing faster than the business.
We deployed a multi-agent SAP BTP architecture using our autonomous enterprise agent framework. Specialised AI agents handle invoice validation, three-way matching, exception routing, and procurement approval — with human escalation only for genuine exceptions. The agents run 24/7 and learn from every transaction.
Multi-channel invoice and PO ingestion with AI-powered document classification and data extraction
Automated three-way matching, duplicate detection, and compliance validation against SAP MM
Risk-scored approval routing with autonomous decision-making for standard transactions
Native SAP BTP integration posting approved transactions directly to FI/MM with full audit trail
Autonomous Invoice Processing
End-to-end invoice handling from ingestion to posting — with AI validation, three-way matching, and exception routing built in.
Procurement Decision Automation
91% of procurement decisions made autonomously — with risk scoring, policy compliance checks, and full audit trail on every decision.
Continuous Learning
Agents learn from every transaction and human override — improving accuracy and expanding autonomous coverage over time.
Invoice straight-through processing
Up from 12% manual baseline
Autonomous procurement decisions
Zero human touchpoints
Continuous autonomous operation
No shift dependency
Reduction in processing headcount
Redeployed to exception handling
2.3M Documents. 94% Answer Accuracy. Enterprise Knowledge at Instant Speed.
A global enterprise unlocked 2.3 million documents with our RAG pipeline on SAP BTP — accurate, cited answers to natural language queries in under 3 seconds.
A global enterprise had 2.3 million documents — technical manuals, compliance policies, process documentation, and historical project records — locked in file systems and SharePoint. Finding the right information took hours. Critical knowledge walked out the door when senior employees left.
We built an enterprise RAG (Retrieval-Augmented Generation) pipeline on SAP BTP using LangGraph orchestration and HANA Cloud vector store. The system ingests, chunks, embeds, and indexes the entire document corpus — then serves accurate, cited answers to natural language queries in under 3 seconds.
Multi-format ingestion (PDF, Word, SharePoint, SAP DMS) with intelligent chunking and metadata extraction
High-performance vector embeddings stored in SAP HANA Cloud for sub-second semantic search across 2.3M documents
Multi-step reasoning pipeline with query decomposition, retrieval, reranking, and answer synthesis
Natural language interface with source citations, confidence scores, and SAP Fiori integration
Universal Document Ingestion
Ingest any document format — PDF, Word, SharePoint, SAP DMS — with intelligent chunking and metadata extraction at enterprise scale.
Semantic Search at Scale
Sub-second semantic search across 2.3 million documents using HANA Cloud vector embeddings — finding the right answer, not just keyword matches.
Cited, Accurate Answers
94% answer accuracy with full source citations on every response — so users can trust the answer and verify the source in one click.
Documents indexed and searchable
Across all enterprise systems
Answer accuracy on enterprise queries
Validated against ground truth
Query response time
Across full 2.3M document corpus
Reduction in information search time
Hours to seconds
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