The platform that
manages the
human layer in AI.
The model learns from data. The data comes from people. Scoring those people — understanding their quality before they start, and tracking what happens while they work — is what determines whether the data is worth training on. That's what reb∞8 is built to do.
contributors
first batch
languages
operations
Two products.
One connected system.
When you run a post-training cycle, you define the benchmark. What's harder to control is whether the people producing the data actually meet it — and whether they hold that standard across the full engagement. That's what reb∞8 is built to manage.
6-engine evaluation. Scores before deployment. Tracks daily. Auto-throttles on accuracy drop.
Labeling, annotation, moderation across every domain. Every contributor scored. Every batch gated.
Humans aren't unpredictable.
They're unmeasured.
The pipeline knows when the model fails. It doesn't know when the human is about to.
You find out at the benchmark. Which means the training run already happened. The compute is spent. The contaminated data is baked in.
No performance-based routing. Low performers get the same tasks as top contributors until the dataset is already contaminated.
Degradation is silent. By the time your benchmark reflects it, those contaminated batches are already in your training set.
You can specify standards. You cannot enforce them. And when output fails, there's no mechanism — and no one to point to.
Every vendor solves one layer. Nobody connects them.
reb∞8 runs the whole loop — scoring, deployment, daily tracking, output gating — as one connected system. So quality is managed before it fails, not after you notice it did.
Five steps. One continuous loop.
Nothing starts without a defined outcome. Nothing ships without a passing score. Every cycle makes the next one sharper.
The Scored Pilot
4 weeks. Your task type. Your benchmark. Your quality threshold. At the end — a score report that no other vendor can produce, because no other vendor has the scoring system to build it.
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Every contributor scored on your task benchmark before they start — built from your samples, not a generic test we reuse across clients
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Badge Score updated daily on every contributor throughout the engagement — if it drops, their allocation drops before your pipeline sees the output
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Score report at the end — contributor distribution, IAA trend, throttle events, every batch traced to the person who produced it
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4 weeks is enough to show you something your current vendor has never shown you
Currently active: LLM / RLHF teams. Other domains available.
"Running data operations at scale teaches you to recognise quality drift early — before it reaches the output, before anyone has noticed. That pattern is what reb∞8 is built on."
Santosh — Founder
The infrastructure behind reb∞8 — the scoring system, the contributor network, the 7-day deployment — came from building operations that had to work before any product existed.
You know who's on your project — and why
Every contributor is scored against your task benchmark before they touch a single task. Not a generic evaluation — built from your samples, your rubric. The score determines who gets in. That's not the standard. It's what we do first.
Quality problems surface before they reach you
Score updates daily. If someone's accuracy drops, their allocation drops — automatically. You don't find out when the model benchmark drops. You find out while there's still time to do something about it.
A document no other vendor can send you
Score distribution by contributor. IAA trend by week. Every throttle event and why. Not a delivery confirmation — the actual quality picture, traced to the person, the session, the batch. Ask your current vendor for this. See what they say.
Ready to see what scored
contributors actually look like?
Tell us what you're annotating and what good looks like. We'll scope the pilot and show you something your current vendor hasn't.
Or email directly: hello@reboo8.com
Twenty years
watching the same
problem play out.
Before AI training data was a market, it was a workforce operations problem — one that large-scale data teams had been navigating for decades. The gap that exists in AI pipelines today was well understood long before anyone called it HITL.
A pattern that kept
showing up.
Data operations at scale means thousands of contributors running simultaneously, quality degrading slowly until someone notices too late. The fix was always the same: track who's drifting before the output ships. That system got rebuilt by hand on every major project. Nothing existed that did it automatically.
Then AI training data became serious business. Same drift. Same missing layer.
Signal is what happens when that problem finally gets a product.
Data services at scale. Thousands of contributors. Quality tracking rebuilt from scratch on every major project — because what existed wasn't enough.
When attention turned to AI training pipelines, the same problem was there. Unscored contributors. No daily tracking. The benchmark drop as the first signal of something that had already happened.
5,000 contributors assessed. Infrastructure running. 7-day deployment ready. None of it built for the pitch — built because the operation had to work before the product could.
Signal and Tag. One loop. Score the contributor, verify the output. The training data problem finally has a system built for it.
reb∞8 didn't start with a product roadmap. It started with a pattern recognised from years of running large-scale data operations — and the infrastructure that came from managing it.
The 5,000 contributors, the 7-day deployment, the quality reporting — none of that came from a spec. It came from building operations where those things had to actually work.
Get in touch
If you're running a post-training cycle and your data quality picture is a black box — let's talk.
The scoring engine.
Not the tool.
The system.
Signal evaluates contributors before they start and tracks their performance on every task. Every score is built on your task type, your benchmark, your rubric — not a generic assessment.
Resume match, task benchmark, structured interview — calibrated to your rubric. Score determines who gets in. No exceptions made for volume or urgency.
Daily Badge Score updates on every contributor. Accuracy drop triggers automatic throttle — before the batch reaches your pipeline, not after the benchmark reveals it.
Score report at project close. Distribution by contributor, trend by week, throttle events logged. No other vendor gives you this — because no other vendor has Signal.
See Signal running on your task type.
4-week pilot. Your benchmark. Score report included.
The output layer.
Every batch verified
before it leaves.
Whatever your input modality — image, video, audio, text, sensor — Tag produces the labeled output your model trains from. Every contributor scored by Signal first. Quality enforced throughout, not checked at the end.
Before anyone touches a Tag task, they've cleared a task-specific Signal assessment. That's the structural difference between reb∞8 and every other annotation vendor.
IAA tracked per batch. Gold label comparison on every task type. If a batch doesn't clear the threshold, it doesn't leave. Verified output — not output needing a second QA pass.
Badge Score drops mid-engagement → allocation drops automatically. Before your pipeline sees it. Not after the model tells you something is wrong.
Every task, every contributor, every quality decision documented. When something fails downstream, you trace it to the exact person, the exact session, the exact batch.
Start with one task type.
4-week pilot. Your task type, your benchmark. No commitment after.
You are not labeling data.
You are teaching a model
how to think.
Every preference ranking you produce tells a model which answer is more helpful, more honest, more safe. Every annotation teaches it what a stop sign looks like in fog, what a tumour looks like on a scan, what a dangerous instruction looks like in plain language. This isn't support work. It's the foundational layer of how AI learns.
Human judgment
at the hardest tasks
Preference ranking. Safety evaluation. Domain annotation. The tasks where a model cannot evaluate its own output — and a person's judgment is the only reliable signal. You are the quality layer that AI cannot replace itself.
A score that
follows your work
Every task you complete updates your Badge Score. It reflects how consistently accurate your work is — not how fast, not how many. As your score rises, you unlock more tasks, more domains, and higher pay. Quality is the only variable that matters.
Pay that rises
with your score
Most platforms pay on volume. reb∞8 pays on quality. The Surcharge engine links your earnings directly to your Badge Score — so improving your accuracy directly increases what you earn. The better your judgment, the more you make.
Four steps from application
to active contributor.
Apply and tell us what you know
Share your background — domain expertise, languages, prior annotation or evaluation work. No CV required. We are looking for people with real-world knowledge of specific fields, not formal credentials.
Complete the Signal assessment
A task-specific test built for your domain area. It is not a generic IQ test. The questions reflect the kind of judgment you would actually be making on the job — evaluating answers, ranking responses, identifying errors. Your result becomes your starting Badge Score.
Start working — at your own pace
Tasks come to you based on your domain and Badge Score. You choose when you work. There are no minimums and no schedules. High-scoring contributors get first access to the most complex — and best-paying — tasks in the queue.
Score improves. Pay improves.
Every task updates your Badge Score. Consistent accuracy lifts it. The Surcharge engine means your pay rate rises directly with your score — no negotiation, no arbitrary raises. Your output quality is the only thing that determines what you earn.
Language model training
Preference ranking, instruction following evaluation, response quality scoring, safety red-teaming. Your judgment directly influences how a language model ranks helpfulness, honesty, and safety.
Road scene annotation
Bounding boxes, segmentation, keypoints on edge-case road scenarios. The situations self-driving systems encounter least often are the ones they need the most help understanding. Your annotation accuracy is a safety input.
Manipulation & environment data
Trajectory labeling, keypoint annotation, physical environment mapping. Robots learn how to pick up, place, and navigate from human-labeled spatial data. Your annotations teach a machine what a hand should do.
Satellite & field imagery
Crop health, field boundary detection, pest identification from aerial imagery. Agricultural AI systems that improve food yield depend on annotators who understand what healthy crops actually look like.
Content policy evaluation
Policy classification, harmful content evaluation, moderation quality review. The rules that protect people online are learned from human decisions. Consistent, careful judgment here has a direct impact on platform safety at scale.
Defect & quality inspection
Visual defect identification, quality classification, sensor data labeling on production line imagery. Precision matters here in a physical sense — annotation accuracy feeds directly into automated inspection systems that make pass/fail decisions.
Not volume workers.
Judgment workers.
The AI industry has no shortage of people who can label quickly. What it is short of are people who can label accurately — who bring real domain knowledge, careful attention, and consistent standards to every task they touch.
We are not looking for people who want to complete as many tasks as possible. We are looking for people whose accuracy will still be high on task 500 as it was on task 5.
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Domain experts — researchers, clinicians, engineers, linguists, agronomists — who can evaluate AI output in fields they know deeply
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Language specialists — native speakers who can evaluate model output for cultural accuracy, tone, and nuance that automatic evaluation misses
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Technical practitioners — developers, data scientists, and engineers who can evaluate code quality, reasoning quality, and instruction following
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Anyone with strong attention to detail — across any background — who can maintain consistent standards across sustained, complex work
Your knowledge has value.
The AI industry needs it.
Tell us your domain. Complete the assessment. Start contributing to the training data that shapes how the next generation of AI models reason, evaluate, and decide.
Or reach us at community@reboo8.com