AI · Infrastructure
Build the runway before you fly the model.
GPU planning, serving topology, latency budgets, and observability — engineered before traffic, not after the first incident.
Overview
AI infrastructure is where most production AI projects quietly fail. We design for capacity, cost, and failure modes up front — so the system holds up the day the demo turns into a deployment.
What it is
The plumbing under every model that ships.
AI infrastructure is the runtime substrate that turns a model checkpoint into a service: GPUs and the schedulers that share them, routers that pin sessions and cache state, replicas that scale with load, and the observability that tells you why p99 doubled at 14:32.
Done well, it is invisible. Done poorly, every model launch starts a new firefight. We build the boring, durable layer underneath — so the interesting work above it can ship without drama.
Workflow
Inference serving, end to end.
- Client sends a request to the API gateway (auth and quota).
- The router selects the model and tenant pool.
- Inference replicas autoscale and pull weights from the GPU pool with KV-cache.
- A parallel cache stores prompts and embeddings, short-circuiting hot paths.
- Every node emits logs, traces, and metrics into the observability bus.
Deliverables
What you walk away with.
- Capacity model: GPU class, memory, replicas, and headroom mapped to traffic forecasts.
- Serving topology: gateway, router, and replica pool with explicit latency and cost budgets.
- Observability: structured logs, distributed traces, and per-route latency/throughput SLOs.
- Failure-mode runbook: cold-start, cache miss, model-load failure, and circuit-break behavior.
- Cost dashboard: per-request, per-tenant, and per-model unit economics, refreshed nightly.
Pitfalls
How we don't do it.
- Sizing a cluster from a vendor calculator instead of measured token throughput.
- Treating inference as stateless when KV-cache and warm replicas dominate p99.
- Shipping without a backpressure or shed-load policy — the first spike becomes an outage.
- Logging prompts to plaintext stores without retention, redaction, or tenant isolation.
Engagement
How we work with you.
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01
Discover
Workload shape, SLOs, and the cost ceiling that matters to the business.
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02
Architect
Topology, capacity model, and the failure modes you accept by design.
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03
Build
Gateway, router, replicas, and observability — wired into your existing platform.
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04
Operate
On-call playbooks, capacity reviews, and a tuning loop against real traffic.
Ready to design for the day after launch?
Tell us your traffic shape and your cost ceiling. We'll come back with a topology, a capacity model, and a list of the failure modes you should plan for.
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