A call that took two seconds for the pilot team takes twenty with the whole company on it. GPUs idle at night and queue at noon, and nobody can say where the time goes.
A call that took two seconds for the pilot team takes twenty at 9 a.m. with the whole company on it.
GPUs sit idle at night and queue at noon. Cloud instances run all month for a load that peaks two hours a day.
Model, retrieval, network, database. Without measurement each team blames the other, and the fix is a guess.
We instrument the path from request to answer, find where time and money go, and build a serving layer sized to the real load: batching, caching, autoscaling and a cost you can read.
Three parts: measure, serve, scale.
Where each millisecond and each euro goes.
The model as a fast, contained service.
Capacity that follows the load, watched by someone.
Four phases, starting with numbers from your current system.
Measure what runs today.
The serving layer for your load.
Containers, pipelines, tests.
Switch, watch, hand over.
The same AI, faster at peak, cheaper at rest, and no longer a mystery.
A number at the 95th percentile, measured under real load, that the team can put in a service agreement.
Capacity scales down when nobody is asking. The monthly bill tracks the number of requests instead of the number of days.
When a call is slow, the trace shows which stage. When the bill moves, the report shows which team.
| Side by side | Pilot deployment | Optimized infrastructure |
|---|---|---|
| Latency at peak | Grows with users | Held at the agreed percentile |
| Cost | Fixed instances, idle most hours | Follows requests |
| Scaling | Someone adds a machine | A written rule, tested |
| When it breaks | A user tells you | An alert names the component |
| Hardware use | One request at a time | Batched, cached, quantised |
It depends on the model, the load curve and where the data may live. We measure your load first and test the candidate servers or instance types on your real requests. The recommendation comes with the numbers that produced it, and with what would change it.
We do not promise a number before measuring. The assessment phase records a week of your real load and shows where time and cost go; the design phase estimates the improvement from that curve, and the load test confirms it before cutover.
Yes, and that is the usual case. The assessment instruments the current system without changing it. Most improvements, batching, caching, quantisation, a scaling rule, are added to the existing deployment rather than replacing it.
The scaling rule is written from the recorded curve with headroom above the peak, and a ceiling on cost. A burst beyond the ceiling queues rather than fails, and the alert tells you it happened so the ceiling can be revisited.
We are a small team of senior specialists. We pick the right model and the right layer, and we build the least machinery that does the job. You get a call with an engineer, not a sales deck.
