Bring us your most expensive AI workflow.
Most production AI is not a chatbot. Support agents, speech pipelines, vision and document models — if it runs in production at real spend, Slancha prices one verified outcome and lowers it without lowering how often it happens. You end with one number that survives your CFO.
One workflow. One metric. One shipped improvement.
Built for workflows spending $25,000+ a month on agents, models, and tools
Price one workflowReads the traces you already emit — LangSmith · Braintrust · Langfuse · Arize Phoenix · W&B Weave · Helicone · OpenTelemetry · plain logs
The enemy is unpriced intelligence.
Your agents resolve real work at machine speed. Do you know what one successful resolution costs?
Model teams cut token cost. Infrastructure teams raise utilization. Product teams raise resolution. Operations cuts handle time. 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 work 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, prompt, retrieval, tool policy, retries, quantization, batching, hardware, caching, serving, 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 workflow. We establish its true cost per verified successful 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
The no-change result stays on the table. 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 workflow. 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 workflow and ends with the engagement.
We start with one agent because agents already act, spend, and produce measurable outcomes at machine speed. The loop that improves one workflow can improve a fleet. The loop that improves a fleet can improve larger systems — companies, supply chains, infrastructure.