Digital products are only as effective as the backend supporting them: It determines whether processes run without media breaks or errors at the interfaces, whether peak loads are handled, and whether the system can be further developed without interrupting operations. A second user group has recently emerged: AI agents that operate systems not through user interfaces, but through APIs. We develop backends for both—from architecture and integration into existing IT landscapes all the way through to operations.
Backend Expertise Since 1999
inovex was founded in 1999 as an IT services company specializing in databases and middleware. These roots continue to shape how we build backends today: closely aligned with the data, with respect for legacy systems, and with an eye toward operations that don’t just begin at go-live.
Architecture with Durability
Modular monoliths, microservices, or event-driven systems—we structure domains so that teams can work independently, and make decisions based on requirements rather than trends. For more details: Architectures.
Scalability & Resilience
From streaming traffic spikes to geographically redundant clusters: We build systems that respond predictably under pressure—using circuit breakers, gateways, and a service mesh on Kubernetes.
APIs & Integration
REST with OpenAPI, GraphQL for aggregation layers, gRPC for latency-sensitive communication, and MCP servers as access points for AI agents—all documented, versioned, and usable by both partner systems and agents.
Real-Time Data
Event streaming, stream processing, and CQRS connect legacy systems with new applications without requiring both sides to be released simultaneously. Our Data Engineering team handles the further processing of the data.
Operations & Observability
Traces, metrics, and logs via OpenTelemetry, plus service level objectives, error budgets, and cost transparency. This makes backend quality measurable rather than just claimed—available as a platform through our Platform Engineering team upon request.
Security & Compliance
Authentication with OpenID Connect, zero-trust communication, secrets management, and a supply chain with SBOM and signed artifacts—compliant with NIS2 and the Cyber Resilience Act. Learn more under Security.
AI in the Backend
Agents, Data, and Responsibility
Models have become interchangeable—the difference lies in the layer beneath them: data access, permissions, costs, and traceability. That is precisely where backend work comes in. We make existing domains usable for agents and bring AI capabilities into production as operational services, not as prototypes.
Agent Readiness
AI agents should interact with legacy systems via structured interfaces rather than through user interfaces. We place MCP servers in front of existing domains, define tool APIs with clear contracts, and secure every call through permissions, client isolation, rate limiting, and auditability.
AI-Powered Applications
Retrieval-Augmented Generation with vector and hybrid search, LLM gateways for routing, cost estimation, and logging, evaluations and guardrails as part of the deployment pipeline—with the option to use your own models if data must not leave the premises.
AI-Assisted Development
We work with coding agents in our day-to-day work—using spec-driven development and review guardrails that ensure quality rather than replacing it. We pass this practical experience on to the team through Agentic Coding Coaching.
Building a modern platform for microservices in a historically grown infrastructure is not an easy undertaking, neither in theory nor in practice. However, the joint project between 1&1 Mail & Media GmbH and inovex shows that such a project can not only succeed, but can even serve as a kind of role model for the business unit.
EXARING AG proves that linear TV and innovation are by no means mutually exclusive. The company worked with inovex GmbH to develop the waipu.tv streaming service.
In a joint project with inovex, CBC would like to create a secure and innovative platform for the upcoming challenges in the TV / entertainment market. The aim is to move the platform to the cloud, to restructure the topics and teams and to switch to an agile way of working.
We tailor our tools to your specific requirements, target architectures, and team—without being bound by technological dogma.
On the JVM, we work with Java and Kotlin, relying on Spring Boot, Quarkus, and Jakarta EE—tried-and-true solutions for systems that need to grow and be operated over many years. We use Go for cloud-native services and infrastructure tooling, and Rust where performance and memory safety must go hand in hand.
In the Microsoft environment, we develop with .NET, and when frontend and backend teams collaborate, we use TypeScript and NestJS on Node.js, Deno, or Bun. The choice of language is determined by requirements, the team, and the operating model—not by habit. We always keep all components up to date with the latest versions supported by the vendors.
We use relational databases such as PostgreSQL as our data foundation, NoSQL for specific access patterns, and vector search for semantic use cases—the prerequisite for retrieval-augmented generation. For data in motion, we rely on Apache Kafka, stream processing, and event-driven patterns such as CQRS; as a Confluent partner, we also provide operational support.
When it comes to data exchange, we carefully select the appropriate technology: REST with OpenAPI for proven and reliable access, GraphQL for aggregation layers and backends for frontends, gRPC for internal communication with tight latency constraints, and MCP servers as access points for AI agents. For us, API gateways, versioning, and binding contracts are part of the architecture—not just documentation.
Kubernetes serves as the runtime environment, supplemented by Service Mesh for traffic and encryption between services, as well as Resilience4j and Spring Cloud Gateway for fault tolerance. Infrastructure as Code and GitOps make deployments reproducible—if desired, as an internal development platform via our Platform Engineering team, with operations managed by DevOps.
Operations are observable via OpenTelemetry for traces, metrics, and logs, analyzed with Prometheus and Grafana, and governed by Service Level Objectives and error budgets. Cost transparency and energy efficiency are also integral to every architectural decision.
On the security front: OpenID Connect and OAuth for authentication, mTLS and zero-trust principles between services, secrets management, and Policy as Code. In the supply chain: SBOM, signed artifacts, and dependency checks—the foundation for compliance with NIS2 and the Cyber Resilience Act. In-depth topics: Security, Digital Quality, and Sovereign Cloud.
Backend architecture is currently changing in two key areas: systems must be capable of interacting with AI agents, and operations—such as the supply chain—must be verifiable. Through our cross-project discussions, we continuously evaluate which of these elements belong in client projects—and which are just hype.
Most backend projects don’t start from scratch, but rather in an established environment with long release cycles and limited test coverage. We modernize incrementally: breaking down domains, introducing interfaces, keeping data in sync, and shifting responsibility bit by bit—until the legacy system can be decommissioned without any downtime.
Modern platforms must be designed differently so that they can fulfill their purposes—more agile, more granular, and more flexible, but always domain-specific and tailored to needs.
The reason is almost always the same: More and more areas of the economy and society are driven by data-driven services, and the growth in services, data volumes, interfaces, and dependencies is overwhelming monolithic architectures, the basic principles of which date back in part to the early 2000s.
Our replatforming strategy outlines how to plan this journey—from analyzing the current state to defining goals and implementing changes. The results are measured using metrics that the business understands: time-to-change, deployment frequency, downtime, and cost per request.
