Make 1 million operational decisions per hour.
Calibrated, machine-readable judgments your systems can act on. No text generation. No hallucination. $0.04 per thousand calls.
Running decision workloads at
Built for the throughput your ops team actually needs.
Airmino returns a single calibrated choice from a set you define at request time — never free-form text.
Everything an evaluator needs, at three levels of detail.
Technical buyers skim, scan, then dig. This page is structured so all three happen without a sales call.
The headline
1M decisions/hour at $0.04 per thousand. Calibrated, schema-valid, no hallucination. That's the whole pitch.
The shape of it
You define options at request time. Airmino returns one choice plus a confidence score and a machine-readable rationale code. Your systems act on it directly.
The architecture
Calibration methodology, benchmark harness, failure modes, deployment topology, and the full comparison against prompted LLMs and rules engines.
Your decision layer is the bottleneck nobody budgets for.
Rules engines don't generalize
Every new edge case means another branch, another deploy, another quarter of maintenance. The rule tree grows faster than the team maintaining it.
LLMs don't fit operations
Prompted models are slow, expensive, and return text you have to parse. When parsing fails at 3am, someone gets paged. That's not a decision layer — it's a liability.
Define choices at request time
Airmino takes your options as input and returns exactly one, with a calibrated probability. Add a new option by changing the payload — not by retraining a model.
How it compares, measured the same way.
All figures from the open benchmark harness. Methodology and raw traces available in the architecture paper.
| Metric | Prompted LLM | Rules engine | Fine-tuned classifier | Airmino |
|---|---|---|---|---|
| p50 latency | 1,800ms | 12ms | 40ms | 90ms |
| p99 latency | 6,400ms | 30ms | 120ms | 140ms |
| Cost per 1,000 | $3.20 | $0.01 | $0.02 | $0.04 |
| Parseable output | 71% | 100% | 100% | 100% |
| Hallucination risk | High | None | None | None |
| New options without retraining | Yes | No | No | Yes |
| Calibrated confidence | No | — | Varies | Yes |
Harness: 50,000 held-out decisions · 3 domains · single A100 node · batch size 1
The questions your technical evaluator will raise.
Answered here so you don't need a call to get to the interesting part.
How is this different from a classifier?
A classifier is trained on a fixed label set. Airmino takes options[] as a request parameter, so the same model handles "approve/hold/decline" at 9am and "tier_1/tier_2/tier_3" at 9:05. No retraining, no redeploy.
What's the accuracy versus an LLM?
On structured decision tasks we match or exceed prompted GPT-class models on top-1 accuracy, with calibration error under 2%. Full confidence intervals and per-domain breakdowns are in the benchmark appendix.
Can it run inside our infrastructure?
Yes. Deploy in your VPC or fully self-hosted. The model is a single container with no external calls — no data leaves your network, no egress costs, no third-party subprocessor.
What's the failure mode?
Airmino can only return one of the options you supplied, in a fixed schema. It cannot invent a category or emit free text. When confidence falls below your threshold, it returns escalate: true instead of guessing.
Which workflows does this fit?
Anywhere a system must pick one option from a set you define: fraud triage, support ticket routing, loan pre-qualification, offer selection, content-moderation appeal. If the decision fits in a request payload and the answer fits a schema, it fits Airmino.
What it looks like in production.
Fraud review time cut from 4 hours to 90 seconds
Auto-adjudicating 340k claims a month without new rules
Ticket routing that adapts when the taxonomy changes
Run 10,000 decisions on your own data.
No credit card, no sales call. Upload a sample, define your options, and see the calibration report in under ten minutes.