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. No rate card.

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 →

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)
CogniPod Framework
PROTOTYPE

Proof of Concept: AI-Powered Blockchain

Exploring secure, decentralized intelligence. This Proof of Concept investigates how Blockchain and Artificial Intelligence can be combined for immutable, transparent, and auditable workflows on top of our proven Java architecture. It is an experimental prototype — not a production product.

Key Capabilities Demonstrated

Decentralized AI Execution

AI models executed via smart contracts with full auditability and immutability.

Intelligent Smart Contracts

AI-enhanced automation for dynamic, context-aware business logic.

Secure Data Collaboration

Privacy-preserving data sharing that fuels powerful AI insights.

Enterprise Java Integration

Seamless connection to Spring Boot, microservices, Kafka, and hybrid cloud environments.

Built with Java · Spring Boot · Blockchain (Hyperledger/Ethereum-compatible) · Modern AI tooling

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.

Mission

Our Mission

Delivering transformative technology solutions that enable sustainable growth and competitive advantage.

Our Vision

To be the global leader in secure AI and Java consulting services.

Oliver Bodemer

About

Meet Oliver Bodemer

Founder & Lead Architect 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.