08 / Services
Self-hosted AI infrastructure
I bring AI inference under your control when privacy, cost, latency or availability require dedicated infrastructure.
Explore the capability →01 / The problem
Problems
- Sensitive data or documents should not be sent indiscriminately to external AI services.
- Cost, latency or availability of external APIs become difficult to control once AI becomes a permanent product layer.
- The model works in testing, but there is no reliable service managing serving, access, monitoring and application integration.
02 / The solution
What
I design and operate the path that takes a model from hardware to application: GPU runtime, serving, APIs, monitoring and integration. The goal is not simply to run an LLM locally, but to turn it into a stable service applications can rely on.
What I build
- Model serving on GPU infrastructure with runtime and configuration suited to the real workload.
- Internal APIs separating applications, models and infrastructure behind a stable boundary.
- Monitoring, operations and an update path to keep the service usable over time.
03 / What you get
Deliverables
- 01
A working inference endpoint that authorised applications can integrate with.
- 02
Operational configuration and documentation covering startup, updates, limits and dependencies.
- 03
A verified baseline for load, latency and service behaviour on the first real use case.
05 / How I work
Process
- 01
Understand — Workload, data, applications, privacy constraints and expected outcome.
- 02
Structure — Model, capacity, access, application boundaries and operational criteria.
- 03
Build — GPU runtime, serving, APIs, operational security and monitoring.
- 04
Validate — Real workload, latency, errors, recovery and application integration.
Have something that needs to become real?
Tell me the starting point: a problem, an idea, a process or a brand. You don’t need a perfect brief — just know what needs to change.