The platform on which AI agents run in production. In your infrastructure.
Most companies piloting AI agents do not fail because of the model. The models work. They fail because of infrastructure: no governance framework, no audit trail, no tenant isolation, no deployment concept, no integration into existing systems.
A pilot on a laptop is not a production architecture. Gosign infrastructure is the layer that turns an LLM experiment into an operational system.
Cloud LLMs: Azure OpenAI, Google Vertex AI -- EU regions with DPAs. Self-Hosted: Llama, Mistral, DeepSeek, gpt-oss on own hardware. Hybrid: sensitive data self-hosted, standard workloads cloud. Model choice is a balance between performance, cost, data protection, and latency.
Retrieval Augmented Generation -- how agents access enterprise knowledge. Semantic chunking, metadata enrichment, hybrid search (vector + keyword), source citation in every answer, regular re-indexing. Vector database: pgvector, Qdrant, or Weaviate.
n8n or Camunda: open-source workflow engine, self-hosted, no vendor lock-in. API Gateway: unified entry point with rate limiting, authentication, logging. Queue system for batch operations. Event system for real-time reaction to incoming documents and escalations.
Azure (EU): AKS, Azure SQL, Azure OpenAI, Blob Storage. GCP (EU): GKE, Cloud SQL, Vertex AI, Cloud Storage. Self-Hosted: Docker/Kubernetes, PostgreSQL with pgvector. Hybrid: combination by data classification. All layers above remain identical.
The infrastructure includes Governance by Design:
Audit trail at infrastructure level (not just application level)
Row-Level Security at database level
Encryption at rest and in transit
RBAC across all components
Cert-Ready Controls as technical data objects
Our AI engineers hold Microsoft Azure AI certifications. Deployment options include Azure, GCP and fully self-hosted infrastructure – the architecture decision stays with the client, not the vendor.
| Component | Technology | Why |
|---|---|---|
| Workflow engine | n8n, Camunda | Open source, self-hosted, no vendor lock-in |
| Database | PostgreSQL + pgvector | Enterprise-ready, RLS-capable, vector search integrated |
| Backend | Python, TypeScript | Proven for ML workloads and API development |
| Frontend | React / Next.js | For dashboard, chat UI, Auditor Portal |
| Containers | Docker, Kubernetes | Standard for cloud and self-hosted |
| API | REST, GraphQL | Integration with existing systems |
| Auth | Supabase Auth / OIDC | SSO-capable, enterprise identity providers |
| Monitoring | Prometheus, Grafana | Open source, self-hosted |
The entire infrastructure belongs to the client. No SaaS, no hosting at Gosign, no ongoing license fees for the platform. Open-source stack where possible. Proprietary components only for the LLMs themselves -- and there model-agnostic.
After 12--18 months, you operate the infrastructure independently.
No. The architecture supports hybrid deployment. You can process sensitive data self-hosted and use cloud services for less critical workloads.
Azure and GCP with EU regions. The architecture is cloud-agnostic -- switching providers only changes the Infrastructure Layer, not the business logic.
ChatGPT, Claude, Gemini, Llama, Mistral, DeepSeek, gpt-oss and more. Open source or commercial models. Self-hosted via Ollama/vLLM -- including OpenAI's own open-weight models, fully runnable in your infrastructure.
For open-source models like Llama, Mistral or gpt-oss, GPU hardware is required. gpt-oss 120B runs on a single H100, gpt-oss 20B on 16 GB consumer hardware. Sizing depends on the model and usage load. We advise on hardware selection.
Talk to us about your architecture.
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