Over 5,000 projects. Today the engineering foundation for AI infrastructure.
Gosign has been developing software for enterprise clients since 2001. Over 5,000 projects for Airbus, Volkswagen, Shell, and others - complex, multilingual enterprise platforms with requirements for security, scalability, and maintainability.
This foundation is the reason Gosign builds AI infrastructure today. Not as a startup that has existed for two years, but as a company with 25 years of engineering experience in enterprise environments.
Connecting AI agents to existing enterprise systems. SAP, DATEV, SuccessFactors, Workday, Microsoft Graph - via REST, SOAP, RFC, or proprietary interfaces. Integration is the point where most AI projects fail. We have solved it thousands of times.
Machine-readable interfaces for agents, dashboards, and third-party systems. REST and GraphQL. Authentication, rate limiting, versioning, documentation.
Architecture design for AI agent platforms. Data model, deployment strategy, scaling concept, security architecture. The architecture determines whether a system is still maintainable in two years.
Web-based platforms for internal users: dashboards, auditor portals, chat interfaces, workflow tools. Responsive, accessible, performant.
Every line of code is written in pair programming. Test-driven (TDD), versioned, review-required. No lone wolves, no knowledge silos. Tests are written before code. Every function has tests. Every merge requires green tests.
Automated build pipeline, automated tests, automated deployment. Every push is tested. Every merge is deployed.
Every merge request is reviewed by at least one other developer. Four-eyes principle for all production changes.
Security is not a retroactive layer. RBAC, encryption, input validation, output encoding - from day 1.
Every change to configuration, rule sets, and system state is documented. Who changed what when - traceable and immutable.
| Area | Technologies |
|---|---|
| Backend | Python, TypeScript, Node.js |
| Frontend | React, Next.js |
| Database | PostgreSQL, Supabase |
| Workflow | Trigger.dev, n8n, Camunda |
| Containers | Docker, Kubernetes |
| CI/CD | GitHub Actions, GitLab CI |
| Monitoring | Prometheus, Grafana |
| Auth | Supabase Auth, OIDC |
The stack follows the project's requirements. We use open-source technologies where possible - for transparency, independence, and cost efficiency.
The engineering principles that apply to enterprise software development also apply to AI infrastructure. An AI agent running in production needs the same discipline as any other enterprise software: tests, deployment pipelines, monitoring, audit trail, security.
The difference: an AI agent makes professional decisions that must be documented and traceable. This requires additional architectural components - the Decision Layer, Governance by Design, Cert-Ready Controls. But the foundation is software engineering.
25 years of that foundation is why our AI infrastructure works.
Our article series for decision-makers implementing AI agents in the enterprise.
Yes. Enterprise software engineering has been our foundation since 2001. Today the focus is on AI infrastructure - but the engineering principles are the same: TDD, continuous pair programming, CI/CD, code review, Security by Design.
Python, TypeScript, React/Next.js, PostgreSQL, Supabase, Trigger.dev, n8n, Camunda, Docker, Kubernetes. The stack follows the requirements - not preferences.
The entire stack is built on open-source technologies. PostgreSQL instead of proprietary databases, Docker and Kubernetes instead of vendor-specific container solutions, Trigger.dev and n8n instead of proprietary workflow engines. Every component is replaceable.
Incrementally via APIs. Existing systems are connected, not replaced. New AI agents run in parallel with existing processes. Every integration step has a rollback plan and is validated in a staging environment.
25 years of enterprise engineering. Today AI infrastructure.
Get in Touch