Inference optimization for AI product teams
We cut your cost per successful task — held to an accuracy bar you set, and proven on your own workload with a benchmark before you commit. Miss the bar and you don't pay.
Services
Fixed-fee engagements tied to real savings — each one benchmarked before you commit. Find the savings, capture them, then keep going.
A fixed-fee review of one workflow: where the money goes, how much is recoverable, and a benchmarked estimate of the savings — before you commit to a build.
We build a smaller, task-specific model and benchmark it against your current one — held to an accuracy bar you approve up front. Miss the bar and you don't pay the back half.
A monthly retainer to keep cutting costs across more workflows as your usage grows — refreshing models and tuning spend over time.
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Behind every engagement is a simple method for finding savings without gambling on quality — three questions, asked in order.
Can the task be reshaped to cost less? Two levers, applied together: break the prompt's task into smaller, simpler sub-tasks — and train a low-rank adapter (LoRA) on a smaller model tuned to the task.
Can a smaller model match the quality you need for this workflow? We distill a compact model from a larger one, then benchmark it against your current model to prove the quality holds first.
Can the same workload run more cheaply once it is deployed? We tune routing, batching, caching, and where the model runs, cutting cost per request without changing the output quality.
Routing and caching alone tend to cap out around 40–60%. Adaptation and distillation are what lift the ceiling past that — where most cost-cutting vendors stop, we start.
Proof
Every firm in this category quotes a percentage. Almost none show the benchmark behind it. The whole business is a measurement business — so here is exactly what we measure, on your own workload, before you commit a dollar.
Not tokens, not raw requests — the price of work that actually lands, measured before and after.
Held to the quality target you set. We publish the delta versus your current model, not just a pass mark.
A cheaper model that's slower in production isn't cheaper. We report the tail, not just the average.
First public benchmark: support-ticket classification. A distilled task-specific model versus a frontier baseline, on an open dataset — full method, numbers, and code, published open-source so you can reproduce it yourself. Want a look before it's public, or the same run on your workload? Book a call.
Who this is for
Precise Infer works best with product and engineering teams that already have AI in production and need a disciplined way to improve inference economics.
How the engagement works
A 30-minute call to understand your workload, spend, and goals.
We map where the cost is and how much is recoverable, with a benchmarked estimate.
We build the optimized model and benchmark it against your current one.
Keep the savings, expand to more workflows, or walk away. Your call.
Why Precise Infer
Precise Infer is built for teams that want experienced engineering judgment applied to a narrow and important problem: reducing inference cost without making production systems fragile.
The work is rigorous, commercially grounded, and respectful of the realities of shipping software inside busy teams. No open-ended advisory. No rebuilds you didn't ask for. A clear decision at the end of every engagement.
FAQ
A short conversation is enough to determine whether there is a real optimization opportunity — and whether it is worth pursuing now.