Who is it for?
Teams expanding an AI product to more users while keeping quality measurable, response times acceptable and spending predictable.
03 / SCALE
Performance & LLMOps
More usage. Controlled costs and latency.
I identify where tokens and time go. Then optimize calls, models, caching and processing, with measurements before and after each change.
Cost per taskp95 latencyObservability
TYPICAL ENGAGEMENT Objectives and measurements agreed upfront
Let’s talk productionWhat you get
- A baseline of latency, costs and errors
- Targeted optimizations: model routing, caching, async processing or quotas
- Load and regression tests within the agreed scope
- Traces, alerts and operating procedures for your team
Example projects
- Agent chains too slow for interactive use
- Inference cost growing with every customer
- Quota errors or API limits during traffic spikes
- Regressions after a model or prompt change
How an engagement runs
Diagnose
I review your code and traces. We identify critical cases, constraints and failures to reproduce.
Deliverable: risks & prioritiesSet the criteria
Expected quality, access rights, acceptable latency and costs. We agree on tests that gate deployment.
Deliverable: acceptance criteriaIndustrialize
I implement fixes and integrations. Business scenarios, errors and permissions are tested at each iteration.
Deliverable: evaluated systemDeploy & hand over
Progressive rollout, alerts, documentation and rollback. Your team knows what to watch and how to respond.
Deliverable: operations & handoverScope, deliverables, timeline and budget are agreed before work begins.
Let’s look at your system
30 minutes · Remote · Technical conversation
Let’s talk production