A tested serving choice
Model, hardware and hosting comparisons using your representative workloads.
Sovereign AI & Inference Engineering
You need control over where AI runs.
Make inference work within your hosting, data and performance requirements.
How it fits together
- Your workload
- Serving design
- Controlled inference
What changes.
We benchmark suitable models and serving options, then implement the inference environment that meets your agreed constraints.
Yours to put to work.
- Scope and acceptance criteria agreed together.
A working inference endpoint
Deployment, access, capacity and monitoring configured for the selected environment.
An operating handoff
Documented data paths, scaling limits, update procedures and task-level costs.
See an example engagement
An example scope, adapted to your environment.
- The starting point: A team needs private inference for a document-processing workflow.
- The work: Compare serving options, benchmark the workload and deploy the selected endpoint.
- The handoff: A running inference service, benchmark results and operating procedures.
How we evaluate the result
- Quality, latency and throughput against agreed workloads.
- Hosting, data handling and access requirements verified in the deployment.
- Infrastructure utilization and cost at the tested load.
A useful place to start
Let’s work through your specific problem.
Start with Sovereign AI & Inference Engineering, scoped to your environment.