{{The solid groundwork for your AI ambitions}}
We build secure, scalable AI platforms with access to data, models and the tools you need. Your teams gain a dependable base on which AI applications emerge productively and repeatably.
A dependable base for your AI
We build secure, scalable AI platforms with access to data, models and tools, on which applications emerge {{repeatably}}.
Platform architecture
We create a scalable, reusable base for your AI applications.
Kubernetes, containers, microservices, API gateway, infrastructure as code
Model access
Teams reach suitable AI models securely and in a uniform way.
Azure OpenAI, model gateway, LLM routing, rate limiting, prompt caching
Data connection
We connect your data to the models securely and keep it current.
RAG, vector databases, pgvector, Azure AI Search, embeddings
Delivery
New AI capabilities go into operation repeatably and under control.
LLMOps, CI/CD, Terraform, Bicep, model versioning
Security and governance
Access, data and cost stay secured and traceable.
RBAC, managed identity, network isolation, audit logging, cost control
Operation and scaling
The platform stays stable and observable under load.
Autoscaling, OpenTelemetry, monitoring, load balancing, SLOs
An AI platform in five steps
Analysis
We capture your data situation, infrastructure and planned use cases.
Analysis
Together we examine the data situation, existing cloud and system landscape and the planned use cases and derive which building blocks are genuinely needed. That avoids isolated one-offs and sizes the platform to real demand.
You receive a solid picture of requirements that makes benefit and effort transparent. This picture settles the building blocks of the target architecture in the next step.
Architecture
We design the scalable, reusable platform base.
Architecture
We design the target architecture on your existing cloud, with container platform, API gateway and infrastructure as code as building blocks. Security and scalability are part of the design from the start.
You receive a viable plan that fits your architecture requirements and grows along under control. This plan determines which building blocks are actually built.
Build-out
We join models, data and tools into the base.
Build-out
We connect models through a model gateway, join your data via RAG and vector databases and set up governed access through roles and permissions. Matched building blocks and tools stand centrally ready for your teams.
Your teams gain a usable base on which first AI applications take shape. This base then passes into repeatable delivery.
Delivery
New AI capabilities go into operation repeatably and under control.
Delivery
We set up LLMOps routines with CI/CD, model versioning and automated rollout, so capabilities are tested and go live under control. Standardised templates carry security and quality along from the start.
Applications can be tested, rolled out and repeated dependably, instead of building each solution from scratch. The rolled-out capabilities pass into monitored operation.
Operation
The platform stays stable, observable and scales along.
Operation
We watch the platform with monitoring, autoscaling and defined SLOs and keep access, data and cost traceably under control. As load grows and new initiatives arrive we extend the base deliberately.
You receive a scalable groundwork that grows with your requirements and stays productive. From daily operation it can be extended to further use cases.
Why {{thinformatics}}
A viable base
A platform on which AI applications emerge repeatably.
Secure and scalable
Security and scaling are built in from the start.
Access to data
Models gain governed, secure access to your data.
Microsoft and open
Azure OpenAI and open models are combined sensibly.
Governance built in
Guardrails, cost and the EU AI Act ride along on the platform.
Productive in operation
MLOps and monitoring keep the platform dependable.
FAQ
Answers to the questions we are asked most often about AI platforms and engineering.
Build your AI platform
We talk about architecture, data access and governance for your AI initiatives.
