Architecture & AI Readiness Assessment
1–2 weeks · Fixed price
A review of the current landscape, the concrete AI levers, and a short roadmap with a presentation.
Discuss this engagement →e-Softworks
Production-ready AI
for existing Java & cloud landscapes
We help enterprises embed controlled AI (local models, RAG, guardrails, data integrity) without exploding headcount or compliance risk.
Focus: critical infrastructure, energy, regulated environments.
Engagements
Engagements
Three ways to start. The scope is fixed. Pricing discussed during assessment call.
Targeted PoC / Implementation Sprint
Fixed price or a clear scope
Examples: local RAG on existing data, a guardrails layer, or a data-integrity component.
Discuss this engagement →Architecture Retainer / Advisory
2–4 days per month
Ongoing architecture sparring, a review of AI initiatives, and coaching for the team.
Discuss this engagement →DE PRODUCT • LIVE
Leitungstreu
Provable 15-minute Istwerte for Redispatch and flexibility — beside SCADA and the Energy Management System (EMS). Cadenza is the fabric; Leitungstreu is the German product line.
Outcome: a quarter-hour Istwert stays checkable beside SCADA and the EMS. Neither is replaced.
Four steps — the EMS stays
1
Sign at the gateway
A Go agent signs the canonical payload hash (Ed25519). Private keys stay at the edge. Tag: Cadenza-Attestation-v1.
2
Verify only
The Spring Boot core never mints source signatures. It verifies, then stores append-only.
3
Merkle + RFC 3161
Leaves are sealed; roots go to an independent RFC 3161 timestamping authority. Not a blockchain.
4
Ampel and Akte
GRUEN / GELB / ROT on the Quellenkarte. Three-party room: Anlage, Vermarkter, VNB; ISB reads.
de.grid.telemetry.v1 · Ed25519 · Merkle · RFC 3161 · Java 21 · Go · PostgreSQL · React
NEW • PROTOTYPE
cadenza
Provable data integrity for critical energy infrastructure — prove operational data is authentic and unchanged — even against the operator, and without a blockchain.
Outcome: a reading can be shown authentic and unchanged, including against the operator, without a blockchain.
How it works — three moves
1
Sign at the source
Every reading is Ed25519-signed at the edge over its payload and metadata.
2
Chain it
Records are bound into a Merkle tree; altering one changes the root fingerprint.
3
Anchor it outside
Roots are timestamped by an independent RFC 3161 authority — a neutral witness.
Ed25519 · Merkle provenance · RFC 3161 anchoring · Java 21 · Spring Boot · Go · PostgreSQL · React
CISSP-aligned security architecture
NEW • PROTOTYPE
Aurora ERP
Enterprise resource planning for one operator, with a separate ledger per company. Finance, people, and time on a server you run. Not a shared multi-customer service.
Outcome: one operator, a separate ledger per company, on a server you run.
Core capabilities
1
Separate ledger per company
Each company keeps its own ledger. Create the company, switch to it, and read income, expenses, and net the same day. Not a shared ledger across customers.
2
Role-based access
Granular permissions with custom roles. JWT-authenticated API, API keys with rate limiting, and optional wallet signatures.
3
Audit & compliance
Append-only security logs, Merkle-provenance audit pages, and tenant-scoped data isolation for regulatory compliance.
Why teams choose it
Separate books per company
Create a company, switch to it in the header, and keep that company's ledger apart from the others.
Post today, read today
Journal entries and a CSV import feed a dashboard of income, expenses, and net, plus GAAP-like and GoB-like management reports.
People and time next to the books
Employees, clients, engagements, timesheets, and utilization sit beside the ledger, with built-in and custom roles.
A server you operate
Aurora runs on premises or in a private cloud. It is built for one operator, with a separate ledger per company.
Java 21 · Spring Boot · PostgreSQL · React · Docker · JWT · Ed25519 (wallet auth) · Flyway
CISSP-aligned security architecture
NEW • PROTOTYPE
Puma
Event-driven etiCORE eTicket for public transport — buy, activate, and validate tickets, with fleet and control-centre operations in one system.
Outcome: issue, activate, and validate a fare in one event-driven system.
How it works — three moves
1
Issue
Purchase a fare product for the chosen zone; the ticket and supporting document are recorded in the system.
2
Activate
Start the validity window when travel begins so the ticket is ready to show.
3
Validate
Check the ticket against an optional vehicle — for inspection and operational control.
Java · Spring Boot · PostgreSQL · React · RabbitMQ (event-driven path) · Docker · VDV / etiCORE-oriented
CogniPod
Introducing CogniPod
The AI-Powered IT Organizational Framework • Networked pods, extensible workflows, unified knowledge platform & AI-driven coordination enriched with lean principles.
Why CogniPod?
- Dynamic networked pods (5–8 members)
- AI-driven coordination & task allocation
- Lean principles: Poka-Yoke, TPS, Kaizen, Jidoka
- 5–10x faster time-to-market (see case study)
AI Architecture
AI-Based Architecture in Practice
Empirical research on embedding autonomous agents as first-class architectural elements — three case studies across software, banking, and healthcare.
What the research shows
When AI agents are embedded at a defined operational bottleneck — not sprinkled onto every process — organizations achieve dramatic improvements in that bottleneck's productivity, quality, and cost. The pattern holds across software development, financial services, and healthcare.
Key findings
- Software company: 61% cycle time reduction, 4.5× deployment frequency, 45% fewer defects ($850K investment, 38% ROI at 18 months).
- US mid-size bank ($45B assets): 90% loan processing time reduction, 30pp AML false-positive reduction, 4.25× fraud detection improvement ($2.45M investment, 22% ROI at 18 months).
- Healthcare network ($25M IT budget): 88% prior authorization time reduction, 58% claims rejection reduction, 62% documentation time savings ($65K investment, 65% physician adoption after co-design).
- Counter-intuitive finding: regulatory density slows adoption but increases eventual impact because legacy processes are more wasteful and regulatory preparation forces data quality investments.
- Five-layer reference architecture: Data & Knowledge → AI Model & Agent → Decision & Policy → Automation & Execution → Observability & Governance. Data flows up; governance flows down.
Five-Layer Architecture
Layer 5: Observability & Governance
Layer 4: Automation & Execution
Layer 3: Decision & Policy
Layer 2: AI Model & Agent
Layer 1: Data & Knowledge
Data flows upward; governance flows downward. This bidirectional pattern distinguishes architecture from tool-level AI.
Capabilities
Our Services
Tailored solutions that drive real business value
AI Consulting & Implementation
Strategy, architecture, model selection and production deployment of AI solutions.
Java Application Development & Optimization
Modern, performant, and maintainable enterprise Java systems — often integrated with AI workloads.
Machine Learning & Generative AI Development
Custom models, fine-tuning, RAG pipelines, inference optimization and responsible AI practices.
System Integration
We integrate AI, Java, and legacy systems to create seamless digital ecosystems. API development, middleware engineering, cross-platform compatibility, and smooth data flows between modern cloud services and on-premise environments are our core strengths.
API Development
RESTful & GraphQL APIs, secure authentication (OAuth/JWT), rate limiting, OpenAPI documentation, and API gateways.
Middleware & Orchestration
Kafka, RabbitMQ, Spring Integration, event-driven architecture, and orchestration with Kubernetes operators or cloud-native tools.
Legacy Modernization
Strangler fig pattern, microservices extraction, data migration (ETL/CDC), hybrid cloud connectivity, and zero-downtime transitions.
About
Meet Oliver Bodemer
Technical Consultant at e-Softworks
Oliver Bodemer is an Enterprise Java Architect with more than 23 years of hands-on experience in building scalable, mission-critical systems. As a passionate AI tinkerer and blockchain enthusiast, he combines deep technical expertise with strategic business insight to deliver transformative solutions.
Holding a Master of Business Administration (MBA), Oliver has authored research on integrating AI and blockchain into enterprise software, insurance, sports technology, and decentralized innovation. His work focuses on practical, high-impact applications — from modernizing legacy Java systems to designing AI-powered architectures and secure blockchain integrations.
At e-Softworks, Oliver is Founder and Lead Architect. Cadenza and Leitungstreu are production systems beside the consulting work: the proof is the software, not a service list alone.
Oliver runs DailyAIWizard, a YouTube channel with 140,000+ subscribers, covering Cloud AI, software architecture, and the CogniPod Framework.
Oliver is the creator of the CogniPod Framework, a transformative AI-driven organizational model that integrates networked pods, extensible workflows, unified knowledge platforms, and lean principles to deliver superior agility, scalability, and innovation in IT project execution.
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