codeCrafters

Services

Software development, modernization and AI for business

We support new ideas and existing systems with custom development, suitable software architecture decisions, integrations, cloud and technical delivery. Our benchmark is software that works in day-to-day operations, remains maintainable and improves real business workflows.

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Technology follows the goal, not the other way around

We start with the business process, the users and the operational demands. The right software architecture, stack and delivery approach follow from there. Whether Java, .NET, TypeScript, Python or cloud-native services: what matters is a solution that remains sustainable and works in daily operations.

Custom software and Full Stack Engineering

For companies whose processes are too specific, too important or too differentiating to rely only on standard software.

When it matters

Relevant when teams depend on spreadsheets, email, manual handovers or tools that do not fit, losing time, transparency or control.

Expected outcome

The result is a usable, maintainable application with clear roles, stable data flows and a technical foundation that can grow.

What we deliver

  • Business and technical discovery
  • UX flows for real tasks
  • Frontend, backend, APIs and data model
  • Roles, permissions and validation logic
  • Documentation and handover

Typical examples

  • Customer or employee portals
  • Backoffice tools
  • Applications for offer, approval or planning workflows
  • Data-driven dashboards and operational tools

System modernization and integrations

For existing systems that are important to the business but have become difficult to maintain, scale or integrate.

When it matters

Relevant when a system cannot simply be replaced, but performance, maintainability, data quality or connectivity must improve.

Expected outcome

The result is a controlled modernization path with lower risk, clearer interfaces and an architecture that improves step by step.

What we deliver

  • Analysis of existing systems and dependencies
  • Modernization roadmap
  • API and integration concepts
  • Data migration and validation
  • Refactoring or staged replacement plan

Typical examples

  • Legacy extensions
  • Integrations with ERP, CRM or internal systems
  • Data cleanup and migration
  • Gradual replacement of individual modules

System and solution architecture

For technical decisions that must support maintainability, scalability, security and collaboration across teams.

When it matters

Relevant before larger investments, when the target architecture is unclear or when complexity grows across multiple systems and stakeholders.

Expected outcome

The result is an understandable target architecture with documented decisions, prioritized steps and clear technical guardrails.

What we deliver

  • Target architecture and system boundaries
  • Technical roadmap
  • Architecture Decision Records
  • Interface and data-flow model
  • Due diligence or technical review results

Typical examples

  • Architecture review before a relaunch
  • Make-or-buy decisions
  • Modularization of existing applications
  • Cloud and integration decisions

Cloud, DevOps and delivery

For software that must be delivered, monitored and evolved reliably.

When it matters

Relevant when releases are risky, environments are missing, quality is hard to measure or development and operations do not work cleanly together.

Expected outcome

The result is more stable delivery, reproducible deployments, better operational visibility and less friction between development, testing and release.

What we deliver

  • CI/CD pipelines
  • Environment and release concept
  • Deployment automation
  • Monitoring and observability
  • Quality checks and handover documentation

Typical examples

  • Staging and production environments
  • Automated builds and tests
  • Release processes
  • Operations-aware quality assurance

How we turn requirements into reliable execution

We do not just process tickets. We translate business goals into technical decisions, make risks visible and deliver increments that can be validated.

Context before solution

We clarify users, processes, data, systems and responsibilities before locking architecture and scope.

Documented decisions

Important technical assumptions, trade-offs and open questions are captured in an understandable way.

Increments instead of Big Bang

Especially in modernization and integration, we prefer steps that can be validated functionally and controlled technically.

Handover from the start

Documentation, quality, operations and evolution are part of the result, not forgotten follow-up work.

Anonymized project contexts

Our experience ranges from business-critical modernization and digital service platforms to specialized AI solutions. Customer names and end-client contexts remain anonymized because of confidentiality, data protection and existing project agreements.

Automotive industry

Step-by-step decoupling of a business-critical monolith

Initial situation

An aging monolithic system managed the connection between customer and vehicle, used an internal rules engine to validate compatibility of services, products and packages, and provided contract and billing data.

Task

Targeted microservices were introduced to replace responsibilities inside the monolith, reduce complexity and make the architecture more stable, scalable and secure.

Specific challenge

Operations could not be interrupted. The system was used globally across several hubs, including EMEA, the United States and China, with different legal conditions by region.

Outcome

The resulting architecture became more scalable, stable and cost-efficient. The project context included data from more than 30 million vehicles in over 90 countries.

MicroservicesRules engineContracts and billingGlobal hubsHigh availability

Commercial vehicle industry

New platform for digital truck services

Initial situation

There was no existing central platform to manage services, products and packages for trucks.

Task

A system was implemented to calculate compatibility between vehicles and services based on product rules and vehicle properties, including contract terms and billing.

Specific challenge

The target architecture used AWS Lambda to reduce operating costs. Complex processes had to orchestrate many consecutive Lambda executions.

Outcome

The result was a scalable, stable and cost-efficient architecture for managing digital services.

AWS LambdaServerlessService compatibilityContractsBilling

IT services company

Platform for specialized AI assistants

Initial situation

Many manual processes used different tools to create documentation, design artifacts and other work products.

Task

A digital assistant platform was developed to use company knowledge from sources such as Confluence, Jira and skill databases, providing specialized assistants for tasks such as PlantUML generation.

Specific challenge

Critical concerns included response times, storage size, runtime behavior and compression of chat histories without relevant information loss.

Outcome

The platform is versatile and extensible. It can connect different data sources, process company knowledge through LangChain and make it usable for specialized tasks such as technical design work.

AI assistantsLangChainConfluenceJiraKnowledge integration

Digital services provider

Modernization of a digital services sales platform

Initial situation

An existing sales platform was outdated, increasingly difficult to maintain and based on technologies that were no longer being meaningfully developed or supported.

Task

The goal was a new platform for selling digital services, based on current technologies and standards, with responsive design and integrated billing.

Specific challenge

Project budget and operating costs for the target platform had to remain especially efficient.

Outcome

The result was a modern, maintainable and extensible platform with intuitive design and lower operational demands.

ModernizationResponsive designBillingCost-aware architectureMaintainability

Technology as a tool, not a dogma

We work with modern stacks and choose what best fits the technical and operational context. The technologies listed here are examples from our broader spectrum; what matters is whether they support operations, teams, security, cost and future change.

Backend

JavaSpring BootQuarkusC#.NETNode.jsNestJSPythonFastAPIGoKotlinRust

Frontend

ReactAngularVue.jsNext.jsTypeScriptJavaScriptHTMLCSSTailwindSassAngular MaterialReact Native

Data

SQLPostgreSQLMS SQL ServerOracle DatabaseMySQLMongoDBRedisETLMigrationReportingData modelingORM

Cloud & DevOps

AzureAWSCloudflareDockerKubernetesOpenShiftHelmTerraformGitHub ActionsGitLab CI/CDJenkinsGrafanaPrometheusOpenTelemetry

AI & Automation

LLM integrationRAGAgentsOpenAIMCPn8nWorkflow automationDocument processingPython data toolsJupyter

Architecture & Quality

OpenAPIAPI designC4 ModelClean CodeHexagonal ArchitectureMicroservicesEvent-driven ArchitectureTDDTestcontainersPlaywrightSonarQubeObservability

Do you have a new or existing system that needs technical clarity?

We can help structure scope, define architecture and plan next steps on a solid business and technical foundation.

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