AI infrastructure and production SaaS

You ship a product with an AI feature or two. It works in the demo, in CI, and at the design-partner stage — then production usage grows and the cost line, the latency tail, the model-swap question, or the on-call incident catches up. The team that knows your customers best is rarely the team free to absorb a serving-stack rebuild or a runtime migration. That gap is where we work.

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Where AI Work Gets Stuck in Production

Two failure shapes recur. The inference bill grows faster than usage and a model release stalls because the unit economics no longer work — or an AI feature passed launch, drifted, and now shows up as a customer ticket rather than a dashboard alert.

The other shape is reach. A model needs to run somewhere it does not run today — a new accelerator, an edge device, the browser — or procurement, a security review, or a board-level governance question needs structured evidence of which model does which task and why, not a slide deck.

Production Inference Cost & Latency

The cheapest AI-cost wins are usually inside the serving stack, not in the model. We profile the workload first — batch sizes, caching, routing, quantisation, kernel choices, serving topology — and surface the changes that move the unit-economics line on the requests you actually run.

Lands in the Inference Cost-Cut Pack — 4–8 weeks, milestone or fixed-price.

Production Reliability & the Eval Harness

Most production regressions are not model-accuracy problems — they are missing evals, missing release gates, missing drift checks, or a workflow that turns a model-version bump into a customer ticket. We build the production-side infrastructure that catches the regression before the customer does.

Lands in the Production AI Monitoring Harness — 4–10 weeks, milestone or fixed-price.

Runtime, Browser & Silicon Porting

A model that runs on the training cluster is not the same artefact as one that runs on the device, the browser, or a constrained edge box. When the AI path on the target does not exist yet, we run a gated feasibility → porting engagement that ends with a working workload, a benchmark on the target, and a runbook your team can re-run.

Lands in the AI Porting & Deployment Pack — feasibility 2–4 weeks, porting of one workload 4–10 weeks, target-dependent.

LLM Selection & Approval Evidence

Procurement, customer security, and board-level governance increasingly ask the same thing: which model, on which task, with what evidence? We build the eval suite and the structured comparison the approval committee can sign against — and the reproducible re-run script that turns the next model swap into a rerun rather than a fresh engagement.

Lands in the LLM Selection Pack — 3–6 weeks, fixed-price.

How We Work With SaaS Teams

Each pack has a fixed scope and a price tied to the outcome, and ends in something your team keeps and can re-run — a benchmark replay, an eval re-run script, a deployment runbook, an evidence map. We do not sell engineer-weeks against a backlog; if the question you need to close does not match a pack, we say so.

Heading into a cost review, a reliability incident, a new deployment target, or an LLM approval? The named pack page is the entry point — or contact us with the question itself and we will route you to the right one.

Start a conversation Tell us the workload