Trust · Certainty · Evidence

Chat tools improvise. You need proof.

ChatGPT, Claude, and copilots can look right on Monday and fail on Wednesday. Raycaster lets you inspect what actually worked for people—and build the same certainty for your workflow.

Published runs · including failures

See the work behind the score.

These live on Eval—the public evidence library. Not only green scores: FDA promo 6/8 Fail, renewables 3/9 Fail, next to a document workflow that passes. Evaluation records, not product demos.

Published runs · live product surface
APEX-Agents · Law5 / 5 · Pass6m 12s

Review warranty claims and update refund amounts

Review the attached warranty claims, then edit the existing product purchases spreadsheet to show the maximum refund amount a customer could receive for each product purchased.

Failure mode · Missed governing agreement constraints

GPT-5.5 · xhigh · Raycaster harness · public tier

Live public run · transcript · files · rubric

Inspect full run ↗
GENERIC CHATRAYCASTER EVIDENCEPrompt → plausible answerNo durable artifact recordConfidence without criteriaFickle across runs · hard to defendTask → trajectory → artifactRubric + expert judgmentPass / fail you can inspectReproducible · measurable · improvable

Your path to certainty

From curiosity to a defensible gate.

01

See what already worked

Open public runs in your domain: the task, the tools, the finished artifact, and the rubric that passed or failed.

02

Name your workflow

CMC change control, contract review, grid modernization, merger models—keep the context attached end to end.

03

Evaluate before you deploy

Turn your hardest work into a private evaluation with acceptance thresholds your team can defend.

Domain and workflow

What work do you need to trust?

Pick a domain. We’ll keep that context when you open evidence, compare tools, or start a private evaluation.

Next

Make AI work for your workflow—with criteria you can defend.

Public evidence shows the pattern. A private evaluation applies it to your files, tools, and acceptance conditions—before agents touch consequential decisions.