Background
Siemens Energy operates in over 150 countries, each with its own HR processes and systems.
An expensive SAP solution was used for collecting and distributing HR data.
Challenge
- Compliance and data access as separate systems
- Manual processes for changes to data delivery
- Over 1,000,000 EUR in annual platform operating costs
- 150+ countries with different data privacy requirements
Solution
Cloud-native, serverless solution on AWS and Snowflake:
- End-to-End Process: Data collection, ordering and approval workflows, data distribution
- Self-Service Data Shop: Teams order data through a web shop, compliance approval automated, Snowflake Secure Views provisioned automatically
- AI Agent Integrations: User describes the desired format, AI agent writes Python transformation code, deploys as Lambda, validates, and automatically brings into production
- Multi-Protocol Delivery: Data distribution via FTP, SFTP, and API, depending on the receiving system's requirements
- Self-Healing: AWS Architect Agent monitors via CloudWatch, analyzes errors, posts root-cause analyses in Slack
- FNTIO as sole implementation partner from concept to go-live
Why FNTIO?
Siemens Energy needed a partner capable of building an entirely new, cloud-native platform. FNTIO brought the experience to meet enterprise requirements and the conviction to use AI agents for process automation.
Phases
| Phase | Timeline | Scope |
|---|---|---|
| Phase 1 | 2024 | Data collection from CRCM + new distribution layer |
| Phase 2 | October 2025 | Full system live, CRCM (SAP) decommissioned |
Results
| Metric | Before | After |
|---|---|---|
| Platform Operating Costs/Year | 1,000,000+ EUR | < 40,000 EUR |
| Cost Savings | - | > 800,000 EUR/year |
| Implementation Time for Changes | Several months | < 2 days |
| Countries Covered | 150+ | 150+ |
Your Starting Point: Data Platform with Self-Service
Your data lives in different systems, changes take months, and platform costs keep rising every year? Here is how the transformation would start for you.
Week 1: Map the data landscape. We gain access to your existing systems and understand where the data lives, how it flows, and where the bottlenecks are. Directly in the system, hands-on.
Week 2: First self-service prototype. A concrete dataset that becomes accessible through a self-service interface. Compliance-ready, automatically provisioned. Your team can test whether the approach works. With real data, not dummy records.
Week 4: Data distribution pipeline running. The core architecture is in place: data collection, transformation, and distribution as an automated pipeline. First AI agents take over integration tasks that were previously done manually.
What Siemens Energy achieved after this point: 800,000 EUR/year in savings, self-service for 150+ countries, implementation times from months down to under 2 days.
