Off the shelf
Sample packs
A priced, documented slice of an existing dataset — schema, quality notes, and licensing summary included — so your team can evaluate against your training stack before committing to anything.
Who We Serve
Seven kinds of teams. One supply chain.
World models, game and computer-use agents, embodied AI, speech, RL research, and expert reasoning — different models, the same bottleneck: real, cleanly licensed data the open web can't give you.
Ecosystem
Emerge builds within the MiraclePlus network alongside its portfolio of AI companies.
01 · World Model Labs
Video of the world is everywhere.
Video with actions and state isn't.
World models need to learn dynamics, not just pixels: what an agent did, and what the world did back. We license gameplay from live and sunset Asian titles and capture trajectories at the engine level — so every frame arrives with the inputs and state that produced it.
What you get
- Frame-synchronized video, inputs, and engine state
- Task goals and outcome labels on every trajectory
- Commercially licensed at source — not research-only
- Live titles, indie studios, and sunset projects
02 · Game Agent Teams
Your agent has watched a million wins.
It has never seen a human recover.
Agents trained on curated highlights fall apart mid-episode. Our trajectories come from real players across skill levels — success, failure, and the recovery in between — with the engine state to tell you exactly why each run ended the way it did.
What you get
- Success, failure, and recovery trajectories
- Real human operators across skill levels
- Video, inputs, state, goals, outcomes — one schema
- Batches scoped by task and difficulty
03 · Computer-Use Agent Teams
Your agent lives in Chrome and Office.
A billion users' workflows don't.
The Asian software stack — super-apps, WPS, LINE, KakaoTalk, domestic enterprise tools, e-commerce back ends — is a blind spot for most computer-use agents, and public supply of iOS traces stops at eval sets. We collect desktop and mobile traces across it, step by step, capturing every layer: screenshot, accessibility tree, DOM snapshot, screen recording, typed action as executable code, and step-level reasoning.
What you get
- Screenshot · a11y tree · DOM · screen recording
- Typed actions as code, normalized coordinates
- Step-level reasoning, failure and recovery traces
- Long-horizon 50–200-step cross-app workflows
04 · Embodied AI & Robotics
Lab demos are staged.
Hands at work aren't.
VLA models need manipulation as it actually happens. We collect first-person hand-operation video — packing, sorting, assembly, tool use — in real workplaces, segmented by task, scene, and device.
What you get
- Egocentric hand-operation video
- Packing, sorting, assembly, and tool use
- Real workplaces, not lab setups
- Segmented by task, scene, and device
05 · Speech & Audio Teams
Benchmark speech is solved.
The way Asia actually talks isn't.
Dialects, accents, noisy rooms, cheap microphones — the speech that breaks production models is exactly what's missing from public corpora, where the largest dialect sets are research-only. We collect consent-cleared speech across Asia — Mandarin and its dialects, Cantonese, Japanese, Korean, Thai, Vietnamese — spontaneous conversation, not just read scripts.
What you get
- Mandarin, Cantonese, Japanese, Korean, Thai, Vietnamese
- Spontaneous conversation, not just read speech
- Segmented by region, age, environment, device
- Signed consent and commercial license per speaker
06 · RL Research Teams
A dataset answers one question.
An environment answers “what if”.
Static SFT data is a means; verifiable reward is the goal. We build resettable, parameterized environments where reward is checked by execution, not opinion — auto-graded, with full engine-state output — delivered as trajectories, task generators, or executable environments, whichever your training loop needs.
What you get
- Programmatic, execution-checked reward
- Resettable and parameterized, auto-graded
- Full engine-state output
- Delivered as trajectories, generators, or executables
07 · Reasoning & Eval Teams
Open reasoning data is distilled.
And your benchmark is already leaked.
Most open reasoning sets are model-distilled and benchmark-contaminated. Our expert network — graduate students, PhDs, and practitioners across medicine, law, finance, and STEM — writes original problems that frontier models fail, with verifiers and step-level rubrics. Both our clients and our contributors know exactly how the data will be used.
What you get
- Expert-authored, uncontaminated problems
- Verifiers and rubric / process supervision (PRM-ready)
- Medicine, law, finance, and STEM domains
- Asian languages and English, fresh time-stamped eval sets
How Engagements Start
Two ways to buy
Built to spec
Paid pilots
A small, priced batch scoped around your model's weakest task — proving task design, schema, quality, and licensing before scale.
20–50 tasks · 50–200 trajectories
1 data schema · 1 quality & licensing report