Evaluation environments · Data · Research
Where agents prove they can do real work.
Raycaster builds the data, environments, and evaluations required to make AI agents reliable at consequential professional work—with tasks, tools, trajectories, artifacts, graders, and expert judgment.
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Failure mode · Missed governing agreement constraints
GPT-5.5 · xhigh · Raycaster harness · public tier
Live public run · transcript · files · rubric
Inspect full run ↗Domain and workflow
What work matters to you?
Choose a domain. We tailor the next evidence, CTAs, and intake around that workflow—without guessing your identity.
Beyond fickle chat tools
The proof is the published run above—not a demo.
Generic assistants improvise. Raycaster shows inspectable tasks, trajectories, artifacts, and criteria—including failures—so deployment decisions and expert judgment rest on evidence.
How experts contribute →Benchmark catalog
Evaluate the work, not the demo.
Benchmark / documents
OfficeQA
Agents that must find, reason over, and act on evidence across realistic workplace files.
Open benchmark ↗Benchmark / spreadsheets
SpreadsheetBench
Spreadsheet tasks scored on the finished artifact—not a plausible-looking chat response.
Open benchmark ↗Benchmark / agents
APEX-Agents
Long-horizon tasks that expose tool use, recovery, and end-to-end completion.
Open benchmark ↗What Raycaster builds
One evidence chain, from task to verdict.
We capture representative professional work, reconstruct it as reproducible agent environments, evaluate complete trajectories and finished artifacts, and use that evidence to improve systems and help organizations deploy them.
Benchmark data
Tasks, source packets, rubrics, and expert judgments drawn from work people actually do.
Evaluation infrastructure
Reproducible harnesses, full traces, artifact inspection, graders, and comparable run records.
Cloud agents
Workspace products that perform the work being evaluated—so research stays honest and teams can automate file work in the same harness.
Choose a path
Three relationships. Not one generic demo.
Tell us whether you need to deploy, improve models, or contribute expertise. The next page adapts.
Evaluate a workflow
Build a private evaluation before deployment—tasks, rubrics, traces, and acceptance thresholds for your work.
Start evaluation intake 02Build for the lab
Novel domains, failure exposure, controllable environments, and holdouts for training and evaluation—not a chat demo.
Start build intake 03Contribute expertise
Fellows, practitioners, and specialist firms turn real work into tasks, rubrics, and judgments—without becoming a chat-labeling gig.
See how contribution worksPrivate evaluations
Turn your hardest work into an eval.
We work with your experts to capture representative tasks, encode the rubric, run candidate systems, and leave you with a durable evaluation program.
Cloud products · same harness
Automate file work. Keep a record the eval can score.
Workspace is where people run agents on documents and spreadsheets. Eval is where those runs stay inspectable. One harness, two surfaces.
Start with evidence
Bring us work an agent needs to get right.
We will make it reproducible, measurable, and improvable—and show the evidence.
Start qualified intake