LLM productionization
The model call works. Everything around it — latency, cost, retries, fallbacks, evals, deploys — is what stands between you and launch.
Start here when the prototype is convincing but nobody can say what it costs at scale, why it fails, or whether yesterday's prompt change made it worse.
When this fits
- The prototype convinces in demos but can't explain its failures in production.
- Provider choice, fallback strategy and cost ceilings are still hand-waved.
- Every prompt change is a leap of faith, because no evals would catch a regression.
What an engagement looks like
- A production-readiness review across latency, cost, prompts, fallbacks and logging.
- A gateway or service boundary that survives a provider outage and a model deprecation.
- A deployment path with queues, caching, retries and dashboards someone will actually watch.
What you leave with
- A system that degrades on purpose instead of failing mysteriously.
- Cost and latency as visible tradeoffs tied to product decisions — not surprises on the invoice.
- Integration boundaries clean enough that the next model swap is a config change.
Case studies behind this
- A multi-provider AI gateway with built-in cost accounting
Model choice became configuration, new providers inherited common controls, and spend became visible while requests were still running.
- Orchestrating long-running inference on serverless GPUs
Transient disconnects no longer killed useful work, and finished or abandoned jobs released GPU resources instead of leaving expensive workers pinned.
LLM systems
For teams past the prompt stage, now facing service boundaries, fallback paths, observability, cost control and evaluation.
Open topicRAG fit check
Retrieval-augmented generation solves a specific problem. Three checks that tell you whether you have that problem — before you build the pipeline.
Read guideCommon AI project failure modes
AI projects rarely die at the demo. They die months later, in production, for reasons that were visible on day one.
Read guide