training data · evaluation · applied engineering · autonomous systems
we build the data, evaluations, and systems behind serious AI.
yuru works across the AI stack: expert training data, benchmarks, and evaluation for teams building frontier models, applied engineering for businesses putting AI into production, and autonomous systems we design, ship, and operate ourselves. every method we sell is one we already run.
// sample rubric
● expert task · rubric v3
accuracy9.2
reasoning8.4
clarity8.9
completeness7.8
who we work with
frontier AI labs
·
enterprise platforms
500+
eval tasks adjudicated
100+
codebases instrumented
counts span client benchmark and verification programs and systems we run ourselves. client specifics stay under NDA, which is exactly how our clients want it.
what we do
one firm, several disciplines. we work wherever AI meets the real world: the data models learn from, the rubrics they're judged by, and the systems they run inside.
01 training data
expert-authored and captured datasets across code, documents, and specialist domains. cleaned, deduped, safety-filtered, rubric-scored, and delivered fine-tune-ready.
02 evaluation & benchmarks
weighted rubrics, LLM-as-judge harnesses, and head-to-head benchmarks run inside the real software under test. ground truth authored first, anchored scoring scales, hard auto-fail criteria, a second reviewer on every verdict.
03 applied AI engineering
agentic systems, automation, and LLM products for business: designed, integrated, shipped, and kept alive in production. from harnesses that drive real desktop and web apps to browser agents and real-time voice.
04 strategy & fine-tuning
post-training on curated corpora, model selection, and eval design, with honest scoping of what AI can and can't yet do for your organization.
how we grade data
quality data is a pipeline, not a purchase. ours turns raw examples into a quality-ranked training set you can fine-tune on directly.
01
ingest
normalize raw examples into a fine-tune-ready schema: messages plus full tool-call and function signatures.
02
clean & safety-filter
dedupe, strip low-signal and generic responses, and remove unsafe content before a single dollar is spent on grading.
03
score on a weighted rubric
every record graded on multiple weighted dimensions by an LLM-as-judge that must reason first, then score, with hard auto-fail criteria for the failure modes that matter.
04
rank & select
read the full score distribution, keep the top band, and hand back a quality-ranked set with the per-record scores that got it there.
how we run evals
a benchmark you can trust is run in the environment the model actually works in, then adjudicated twice. this is the loop behind our benchmark and verification work.
01
ground truth first
domain experts author the expected answer before any model output is seen. scoring happens against a spec, not a vibe.
02
run in real software
custom harnesses drive the actual editors, browsers, and platforms under test, capturing every output, its latency, and the telemetry behind it.
03
score on anchored rubrics
every dimension is scored against written anchors, competing systems are scored independently, and judges justify before they score.
04
verify twice
a second reviewer re-checks every verdict against the evidence, quoted verbatim. nothing ships on a single opinion.
we build it too, not just grade it
the methods above aren't slideware. they come out of shipping and running our own autonomous AI, at the edge of what agents can reliably do.
a fully autonomous AI vtuber that live-streams unsupervised: drives real apps by browser automation, talks in real time, remembers, and sets its own goals. our proving ground for agent reliability, and where the grading pipeline above was forged: 26 corpus versions generated, judged, and ranked so far.
our native desktop app for agentic coding with claude code: 4 parallel agents, 50+ shortcuts, 18 themes, vim mode, voice, multi-provider. free demo, pro from $4/mo.
market analysis with dual-model sentiment, real-time prices, and intraday charts. a working example of multi-model systems in production.
more from the lab: voidsynth (generative synth) · glorps (tts for streamers) · toast hell (game) · blackjack