Vantage RuntimeAI · Preflight
Preflight — size the scenario
Preflight drafts a custom scenario, forecasts eval cost, and scores candidates before production spend. That sizes the path — the product is re-gating the same rubrics in CI on every change, not a one-shot pick.
Part 1 · Core Organizational Settings
What a wrong model pick costs — and what proof costs
Preflight is priced per decision — one test flight per use case. Dial headcount, adoption, and decisions/person; exposure follows those numbers.
Decision mix = what kinds of decisions your org makes. Proof level = how hard each decision is tested.
Standard — Normal scenario depth for most model picks
Best for: Day-to-day product decisions
Illustrative only — not expected savings or a P&L forecast. Dial volume to see order-of-magnitude stakes; Part 2 gives real scores and prod-use / wrong-model overpay on your scenario. How these numbers are modeled →
Now test candidates in your context
Describe your real decision, refine the scenario, then get per-model scores and cost on your scenario — before any production spend.
Using portfolio settings from Part 1 — examples and turns follow your mix and proof level.
1. Your open question
What if / how could we / what would it cost to — in your context. Paste workflow notes, policies, or constraints so the scenario matches the decision.
Start from an example
Or write your own question below — Preflight will draft a custom scenario from your workflow, policy, or candidate notes.
Dialogue length for each model. Prefer raising turns so cheaper models can finish the job before you spend up to larger models.
2. Refine your scenario
Edit the draft below or paste more detail, then regenerate. This is your test-drive script — tune it until it reflects how the decision actually gets made.
Setup & estimates collapsed while you review or search
3. Cost & scores
Model search
Starting…
| Model | Status | Rubric | Pass | Actual $ | Results |
|---|
For search engines and LLMs
Vantage Preflight sizes a custom scenario and forecast before spend. The product is continuous CI re-gating with the same rubrics — not a one-shot model pick.