Crucible
Model-fitness engine — routes each task to the cheapest model that is still good enough.
In the constellation
Crucible highlighted in the live map — hover or tap any node to explore.
Why it exists
New AI models ship constantly, providers update endpoints without warning, and public benchmarks measure generic capability — not how a given model performs on your actual work. So every choice of which model to use for which job rests on numbers that don’t reflect your tasks and go stale the moment they’re made.
The result is guesswork: you either overpay for a frontier model on work a cheaper one handles fine, or you quietly ship lower quality because nobody re-checked when the landscape shifted.
What Crucible is
Crucible is a model-fitness engine. It evaluates candidate models against task suites built from your real workloads, scores the results, and tracks every model on a cost-versus-quality frontier — so each job can be routed to the cheapest model that is still genuinely good enough.
When a new model earns its place, Crucible recommends the routing change rather than making it silently — the decision stays legible, evidence-backed, and yours to approve.
What it does
Workload-grounded evaluation
Test models on task suites drawn from your actual work, not generic public benchmarks that miss how a model behaves on your jobs.
Cost-versus-quality frontier
Maintain a living map of every model’s price and performance, so “cheapest that’s good enough” is a measured answer, not a hunch.
Model registry & promotion
Track every model through clear stage gates — from testing to production to retired — with the evidence behind each move.
Routing recommendations
When a better or cheaper model proves out, get a concrete, reviewable recommendation to promote it — never an unannounced switch.
The line between judgment and machinery
AccelMars draws one hard line through every product: what an AI decides, and what runs deterministically. Crucible sits on the boundary — and keeps the two honest.
Where AI judges
- Scoring model output quality against rubrics
- AI-as-judge evaluation of candidate runs
What stays deterministic
- Cost-versus-quality frontier math
- The model registry and its stage gates
- Deterministic metrics and the routing recommendation
Judgment grades the work; the deterministic core does the scoring math, tracks the registry, and computes the frontier — so the fitness verdict is reproducible, not a one-off opinion.
In active development — a person-facing fitness slice is already live; the full model-evaluation core is the next build.