Solution
AI Application Infrastructure
Serve models next to your own data, with the operational plumbing included.
GPU compute, model serving, retrieval and gateway infrastructure for teams building AI applications on sensitive internal data.
The situation
A promising AI prototype cannot go to production because the data cannot leave, the GPUs are not there, and nobody owns the serving stack.
Outcomes
What changes
- A path to production
- From notebook to a served endpoint with versioning and monitoring.
- Data kept in place
- Retrieval and inference designed around where documents are allowed to live.
- Predictable cost and capacity
- GPU allocation per team, with quotas and visibility.
- Operational control
- Audit logging, rate limiting and access control on every model call.
How it works
From first conversation to running platform
- 01
Define the boundary
Which data must stay in, and which model capability you actually need.
- 02
Stand up serving
GPU nodes, model serving, gateway, vector store.
- 03
Wire the pipeline
Ingestion, embedding, refresh and evaluation.
- 04
Operate
Monitoring for latency, quality, cost and drift.
We are deliberately unromantic about AI infrastructure: most of the work is data pipelines, access control and capacity planning, and most of the risk is in what happens after the first demo impresses everyone.
Industries
Most relevant for
Discuss ai application infrastructure for your organization
A short call with an engineer, an architecture we can put on a page, and a straight answer about what it takes to run it.