The Problem
What It Looks Like
- • Monolithic application that's become unmaintainable
- • Can't deploy individual features — everything ships together
- • Scaling requires scaling the entire application
- • Single point of failure affects everything
- • Different teams blocked waiting for each other
What It Costs
- Release cycles measured in months, not days
- High coordination overhead across teams
- Scaling costs 5-10x higher than necessary
- Innovation blocked by architectural constraints
- Team velocity decreases as application grows
The Real Risk: Monoliths don't scale — not the application, not the team. You'll hit a wall where adding engineers doesn't increase output. Competitors with modular architectures will ship 10x faster.
The Solution Approach: AI-Driven Decomposition
Use AI to understand your monolith before you break it apart. Extract domain boundaries intelligently, validate service boundaries, then modernize systematically.
Vibe Document: Understand the Current State (1-2 weeks)
Create the blueprint for decomposition by understanding your monolith's real structure and pain points.
- • Current architecture, teams, and deployment model
- • Pain points: slow deployments, scaling bottlenecks, coordination overhead
- • Implicit business domains and how they interact
- • Data flows and dependencies between systems
- • Code Archaeology: Identify hidden service boundaries
- • Dependency Mapper: Visualize data and function coupling
- • Team Topology: Match code structure to team structure
Result: A shared understanding of what needs to change and why. This document guides all future decisions.
Validate: Prove Service Boundaries (1-2 weeks)
Don't guess at service boundaries. Validate them against real code to ensure clean extraction.
- • Analyze code for cross-domain coupling
- • Identify shared data structures and dependencies
- • Map team responsibilities to proposed services
- • Prioritize services by value and extraction difficulty
- • Service map with clear boundaries
- • Extraction roadmap (which services first)
- • Data migration strategy per service
- • Integration points and contracts
Why this works: AI analyzes coupling across 100k+ lines of code in hours. You get validated boundaries, not guesses. Prevents expensive extraction mistakes.
Context (Reverse-Engineering): Extract Domain Knowledge (2-4 weeks)
Before extracting a service, AI reverse-engineers what it actually does from the monolith code.
- • Code Comprehension: Understand service logic from monolith
- • Test Extraction: Reverse-engineer specs from existing tests
- • API Discovery: Identify current internal API contracts
- • Data Mapping: Track data flows and transformations
- • Service specification document
- • API contract (what data flows in/out)
- • Database schema for service
- • Business logic explanation
Why this matters: You understand exactly what behavior must be preserved when you extract. No surprises when the new service goes live.
Spec: Define Requirements & Architecture (2-3 weeks)
Design the new service using AI agents that enforce best practices and document decisions.
- • Converts context doc into structured requirements
- • Identifies edge cases and boundary conditions
- • Documents API contracts for integration
- • Proposes service architecture and tech stack
- • Designs database schema from requirements
- • Documents integration patterns and data flows
Result: Complete service specification with requirements, architecture, and API contracts. Your team can start building with confidence.
Testing & Security: Build Validation (2-3 weeks)
Generate tests and security validation before building the service. Catch issues early.
- • Generates test suite from requirements (70%+ coverage)
- • Creates golden path tests for legacy behavior validation
- • Defines performance and load test scenarios
- • Validates against security standards (OWASP, encryption)
- • Reviews API design for vulnerabilities
- • Ensures data protection and compliance requirements
Why upfront: Build tests before implementation. Implementation follows test contracts. No late-stage surprises.
Operation: Build, Deploy & Validate (Ongoing)
Implement the service with AI-assisted development, then safely transition from monolith to microservice.
- • Code Generation Agent scaffolds service
- • Builds against test suite (TDD)
- • Integrates with existing APIs
- • Run against golden path tests
- • Run new service parallel with monolith
- • Compare outputs before switchover
- • Documentation Agent auto-generates docs
- • Operation runbooks created
- • Knowledge transferred to your team
Value Delivered
Strategic Outcomes
- Teams can move independently
- Cloud-native deployment options
- Targeted scaling per service
- Easier to adopt new technologies
Typical Engagement Model
Phase 1: Advisory & Enablement (6-8 weeks)
Foundation phase applies to all engagements. Get your team ready before execution begins.
- • Architecture audit and dependency mapping
- • Team structure and skill assessment
- • Prioritise services for extraction
- CHF 8-12k
- • Set up AI agent infrastructure
- • Define governance and quality gates
- • Establish testing and compliance framework
- CHF 12-18k
- • Generate service specifications
- • Define extraction roadmap
- • Document API contracts
- CHF 8-12k
Advisory & Enablement Total: CHF 28-42k, 6-8 weeks. Your team completes this foundation before choosing an execution path.
Phase 2: Choose Your Execution Path
Build Your Own
Your team takes the specifications and builds independently. We provide guardrails and support.
12-24 months (team-dependent pace)
CHF 28-42k (Advisory & Enablement only)
- • Complete service specifications
- • AI agent framework ready to use
- • Quarterly advisory support (optional)
- • Your team owns the execution
Best for: Teams with strong engineering capacity who want to drive modernization internally.
Build-Operate-Transfer
We build and operate the services while your team learns and gradually takes over.
CHF 25-50k: Architecture, platform, first 2-3 services
CHF 120-300k: 20-30 services extracted, team training
CHF 15-60k: Handoff validation and support
Total Cost: CHF 188-452k | Timeline: 18-24 months
Best for: Teams needing hands-on support, faster execution, and proven results.