Bring us the models nobody watches.
Most production inference is not a chatbot. Slancha optimizes the speech, vision, image, video, and document models that do the work — and lowers what one verified outcome costs without lowering how often it happens. You end with one number that survives your CFO.
One pipeline. One metric. One shipped improvement.
Built for workflows spending $25,000+ a month on agents, models, and tools
Price one pipelineReads the traces you already emit — LangSmith · Braintrust · Langfuse · Arize Phoenix · W&B Weave · Helicone · OpenTelemetry · plain logs
Nobody has priced this work.
Your models transcribe calls, render images, and read documents at machine speed. Do you know what one verified result costs?
ML teams shrink models. Infrastructure teams raise utilization. Product teams raise acceptance. Operations cuts turnaround. Finance cuts budgets. Each team can win while the whole system gets worse — because no one owns the denominator that crosses their boundaries.
The loop is the product.
We observe production inference through the traces you already emit — LangSmith, Braintrust, Langfuse, Arize Phoenix, W&B Weave, Helicone, OpenTelemetry, or plain logs. We join spend to a verified result. We find the binding constraint — model, quantization, compilation, batching, hardware, serving, caching, or routing. We test a bounded change against a predeclared baseline and quality floor. We ship it with rollback. Then we come back and grade the realized result against the forecast.
The Production AI Optimization Sprint.
21 days. $15,000, fixed. One production inference pipeline. We establish its true cost per verified outcome, test a bounded set of changes, and ship one approved improvement when the evidence supports it.
Preregistered baseline and evidence contract
Cost-per-outcome decomposition
Experiment record for every tested change
One reversible shipped improvement
Realized-versus-forecast scorecard
Ranked 90-day backlog your team can run without us
The no-change result is a real outcome. Slancha optimizes your system, not its own activity — if the evidence says leave it alone, that is the report. The fee buys the measurement; the shipped change is the upside.
How every engagement is graded.
This workflow cost X per successful outcome. Constraint Y caused the waste. We changed Z. The controlled result improved. The quality floor held. The change shipped. The realized result persisted. That grammar — baseline, denominator, outcome definition, quality floor, intervention, experiment design, rollback, forecast, realized — is the standard every engagement must meet.
Write to Paul Logan or book the scoping call directly. You get a reply within two business days.
A 30-minute scoping call. Pick the pipeline. Agree the outcome definition — drawn from your own source of truth — the metric, and the quality floor.
Preregistered baseline before day zero. The evidence contract is signed first; access is read-only trace data from the stack you already run, under NDA.
Days 1–21: we measure, test bounded changes against the baseline, and ship one approved improvement through your team and your deploy process, with rollback — or deliver the no-change report.
You keep the instruments: the realized-versus-forecast scorecard, graded after the preregistered measurement window rather than on day 21, and a ranked 90-day backlog your team can run without us.
Slancha is run by Paul Logan — paul@slancha.ai, subject line: Production AI Optimization Sprint.
You commit to nothing before the baseline is agreed. The NDA and evidence contract are signed with Slancha, Inc., a Delaware corporation; trace access is scoped to the one pipeline and ends with the engagement.
We include the models nobody watches because that is where spend hides. Speech, vision, and media models already run at production scale with real budgets and measurable outcomes. The loop that prices one pipeline can price a fleet — and most production inference is not a chatbot.