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AI-Native Software Modernization
Make existing software ready for tomorrow.
We modernise business-critical applications, architectures and development platforms – without needlessly rebuilding systems that work.
Capabilities
AI-native software engineering
Build software
for the AI era.
We modernise business-critical enterprise software and integrate AI into systems, processes and software development.
Swipergy combines AI engineering, software architecture, modernisation and secure delivery – so companies can use the productivity of modern AI without losing control over quality, security and their existing system landscape.
Clients and partners
Software gets built faster.
Complexity does not go away.
AI fundamentally changes the speed of software development. Code is written faster, internal teams become more productive and new features ship in less time.
Yet enterprise software stays complex: architecture, legacy systems, security, interfaces, data, tests, governance and operations do not go away.
That is exactly where Swipergy comes in.
Legacy slows you down
Grown architectures, technical debt and old dependencies make every change expensive and risky.
Delivery does not scale
More code does not automatically mean faster delivery. Unstable pipelines, missing tests and complex releases remain the bottleneck.
AI meets real systems
A prototype is built quickly. Production AI, however, has to work with APIs, identities, permissions, data and existing applications.
Quality matters more
When AI speeds up development, architecture, automated tests, security and technical governance matter even more.
Teams have no capacity for transformation
Internal teams know their systems – but are often fully occupied with features, operations and day-to-day business.
Swipergy takes on exactly these
technical change topics.
Services
Engineering for the next generation of your software.
Three service lines, one foundation: software engineering for complex enterprise systems – with AI as an integral part, not an add-on.
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AI Engineering
Bring AI into real enterprise systems.
We do not build isolated AI demos. We integrate AI into existing data, applications, processes and permission models.
Solution line: Enterprise IntelligenceCapabilities
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AI-Native Engineering & Delivery
Software development for the AI era.
We help engineering organisations put modern AI tools to productive use in their software development – without losing quality, architecture and security.
Capabilities
AI changes how software is built.
But engineering still determines whether it works.
Generative AI can now produce in minutes the code that used to take hours.
That shifts the bottleneck.
Writing code alone no longer decides whether software succeeds. What decides is:
- Architecture
- Integration
- System understanding
- Security
- Quality
- Testing
- Operations
- Technical decisions
That is exactly where Swipergy's strength lies.
We combine modern AI tools with professional software engineering and years of experience in complex enterprise systems.
Solution line · AI engineering
Enterprise Intelligence
Make data, knowledge and processes usable with AI.
Enterprise Intelligence connects company data, knowledge, systems and processes with AI. Employees and AI agents can find and understand information and – within clearly defined permissions – take action.
- Enterprise Knowledge Make company knowledge searchable across sources and queryable with permissions applied (RAG).
- Intelligent Work Assistants in everyday work: research, analysis, documentation, customer interaction, engineering.
- Intelligent Processes Understand documents, classify information, prepare decisions, trigger the next step.
- Agentic Automation AI agents execute defined processes across multiple systems – with permissions, guardrails and human control.
Technical foundation
Enterprise Intelligence is integrated into the existing system landscape – the data, software, APIs, processes, domain knowledge and cloud are usually already there. What is added is the engineering work in between.
How does AI get secure access to company knowledge?
Data sources, identities and permissions are part of the architecture – not bolted on afterwards. Access follows the existing permission model, not a new one next to it.
How are hallucinations and quality kept in check?
The AI works from the company's own knowledge and context (grounding). Answer quality, usage and behaviour are measured with evaluation and observability and improved continuously.
How do AI agents interact safely with production systems?
Through defined APIs with clear permissions, guardrails and human control: critical decisions and actions stay controllable and traceable at all times.
How are data protection and regulation handled?
Data protection, traceability and regulatory requirements are addressed early in architecture and operations – not just before go-live.
How does one use case become a capability for the whole company?
Every solution is built as a reusable capability: further data sources, new teams and new AI agents all build on the same technical foundation.
How we work
From problem to production.
We work project by project: one clearly defined technical problem, one result we take responsibility for – and a path to roll it out afterwards.
Understand
Understand system and goal
We understand the system, the architecture, the risks and the business goal.
Output: technical assessment and a clear scope.
Modernize / Build
Deliver a defined scope
We take over a clearly defined technical area and deliver it.
- Modernising a module
- Building an AI capability
- Stabilising delivery
- Architecture improvement
Integrate
Fit it into the landscape
The solution is integrated into existing systems, data, IAM, APIs and processes.
Output: in production inside your system landscape.
Scale
Roll out what works
What works is rolled out to further systems, teams or use cases.
Output: a reusable capability instead of a one-off.Start small. Ship to production.
Then scale.
How a collaboration can start.
Three entry points, depending on how clear the technical problem already is. Each ends with a result – not with a presentation.
Engineering Assessment
Analyse architecture, codebase, delivery, security and technical risks.
approx. 1 weekModernization Sprint
Not just analyse one concrete technical problem – solve it.
approx. 4–8 weeksEmbedded Delivery
Swipergy takes ongoing responsibility for a clearly defined technical area.
several monthsAbout Swipergy
AI needs engineering.
Data. Interfaces. Identities. Permissions. Security. Software architecture. Quality. Operations.
Enterprise software rarely fails because someone cannot write code.
The difficulty lies in the reality of complex systems: data, interfaces, identities, permissions, security, software architecture, quality, operations.
Swipergy combines these engineering disciplines with modern AI. We do not build isolated demos, but systems that work in real organisations.
Swipergy was founded in 2019 and has since supported companies and public-sector clients through digital transformation – with a clear focus on AI-driven transformation and AI engineering since 2024.
New models will arrive, new tools will disappear. What remains is the ability to bring existing systems, software engineering and AI together. Build that foundation today and you will adopt every new technology of tomorrow faster.
What should your software look like in the AI era?
Whether modernisation, AI integration or engineering transformation:
Let us talk about the technical problem that is slowing your team down right now.