Test the models you are actually considering on your own workload. Weigh quality, cost, and latency against the threshold you set, bring in a blinded expert where a metric cannot decide, and produce a decision record your client, your auditor, or your board can inspect line by line.
Example ranked proof for a transcription task. Deepgram nova-3-medical wins at 0.883 quality and $294 per month, 40% cheaper than the AWS Transcribe incumbent at 0.879 and $487 per month. OpenAI whisper-1 is cheapest at $122 per month but scores 0.820, below the 0.85 quality bar, so it is ruled out.
Scores live in a dashboard your engineers read. A decision has to survive a client, an auditor, or a budget conversation months later, when nobody remembers the sample size or who approved it. That record is the thing RedCrown owns.
Workload, candidates, incumbent, dataset version, model versions, thresholds, methodology version, economics, reviewer verdicts, and per-item evidence, held together as one object with a timestamp.
When a metric cannot decide, a domain expert votes A vs B without seeing model identities. Their verdicts become part of the record, not a side conversation.
No model-vendor money, no margin on the calls you run, and a pinned methodology version stamped on every result. Nobody can buy a better ranking.
Score against your own labeled ground truth, or send it to a blinded human reviewer who votes A vs B without seeing model identities. When you do use a judge, it is pinned and versioned, and every verdict carries the receipt it was based on.
You already do the work. The problem is that it lands as a slide saying "we tested three providers and recommend Anthropic", and the client has to take your word for it. RedCrown makes the evidence the deliverable.
"We evaluated OpenAI, Anthropic, and Gemini and recommend Anthropic."
Two engagements, anonymized. Quality measured on the real client data in each case, monthly cost projected from provider list prices at the same volume, with the sample size and the caveats stated on every card.
Measured on 9 clips. The 0.4-point accuracy difference is within a single clip at that sample size, so the accuracy result is directional and the cost result is the finding.
Measured on 4 notes. The pass-rate gap is a single note at that sample size, so it is directional and the cost result is the finding.
You do not have to move your evaluation stack to get a decision record. If you already ran the comparison in your own harness, a notebook, or a third-party eval platform, import the results and RedCrown normalizes them into the same ranked, reviewable, shareable decision as a native run.
The CLI runs the whole evaluation locally with your keys, or drive it from an agent over MCP.
Your own test suite, a Python pipeline, or another eval platform. Whatever produced the numbers.
Import the results and get the ranked report, per-item receipts, expert review, and a shareable decision record.
A model decision has a shelf life. Providers ship new models and change prices, your prompts drift, your workload changes, and the threshold that mattered last quarter may not be the one that matters now. A decision nobody re-checks quietly becomes a guess again.
Not live yet. Until it ships, re-running is on demand and we will not pretend otherwise.
Run anywhere, on your own machine, with your own keys. You see the ranked answer in your terminal before you make an account, and push only the results you choose.
Terminal session: pip install redcrown, build a dataset, run the eval locally, log in, then push the result to mint a shareable proof link. The ranked output names deepgram nova-3-medical as the winner at 40% cheaper.
$ pip install redcrown $ redcrown build-dataset primock57 --out exp.json $ redcrown eval exp.json --report-json out.json # local $ redcrown login $ redcrown push out.json --proof-link # prints share URL RANKED transcription · cheapest at your 0.85 bar ✓ deepgram · nova-3-medical 0.883 $294/mo ← winner, 40% cheaper aws · transcribe-standard 0.879 $487/mo incumbent openai · whisper-1 0.820 $122/mo below bar proof: app.redcrown.ai/proof/… # no login to view
RedCrown takes no payment, rev-share, or placement fee from any model provider, and takes no margin on the calls you run. You bring your own provider keys, so the calls run under your accounts at your rates. Scoring and ranking run on a pinned, versioned methodology, and every proof records the version that produced it. Read the full neutrality structure.
It would be, on its own. So score against your own labeled ground truth, or send the run to a blinded human reviewer who votes A vs B without seeing model identities. When you do use a judge, it is pinned and versioned, and every verdict links to the receipt it was based on so you can check the grading yourself.
No. The CLI runs the whole eval locally with your keys, and you see the ranked answer in your terminal before you make an account. Only the results you explicitly push become a hosted proof. Anything you do store with us is encrypted at rest and siloed per workspace, and proof links expire or revoke when you say so. We hold no formal certification (no SOC 2, no ISO 27001) and do not claim any. The details are on the Security & Trust page.
You can still prove the cost drop. Score the challengers against your current model's output instead of a labeled answer key, and RedCrown names the cheapest config that matches what you ship today. No labeling project needed. If you do have labels, it scores against those instead and you get a quality verdict too.
Workloads, not tokens. Proving a task is free and unlimited on your own machine. A paid plan keeps a workload proven: hosted proof pages, unlimited magic-link reviewers, re-running on demand, history and ROI rollup, and the live proxy. Team starts at $99/mo for 3 workloads. There is no markup on inference; you pay your providers directly. See the plans.
For hands-on onboarding, security review, or procurement. We reply within one business day.
Run your first workload free, in your browser or your terminal. No sales call, no credit card.