The platform that
manages the
human layer in AI.
Every AI model learns from human-labeled data. reb∞8 scores the people producing that data — before they start, and throughout every task.
One loop. Score first. Annotate after.
Signal evaluates contributors before deployment. Tag produces the output. Quality enforced at every step.
6-stage evaluation. Badge Score assigned at entry, updated daily on live accuracy. Drops → auto-throttle.
Labeling, annotation, moderation. Every contributor Score-evaluated first. Every batch gated before delivery.
Four places quality breaks when the workforce isn't scored.
The pipeline knows when the model fails. It rarely knows 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.
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 on every contributor who worked your data.
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Every contributor scored on your task benchmark before they start — built from your samples, not a generic test
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Badge Score updated daily. If accuracy drops, 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
Currently active: LLM / RLHF teams. Other domains available.
"Quality drift is recognisable. It follows the same pattern every time — quiet accumulation, then a visible failure. That pattern is what Signal was built to catch."
Santosh — Founder
The infrastructure behind reb∞8 — the scoring system, the contributor network, the 14-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.
Four weeks. Your data.
A full quality picture.
Tell us your task type and what good looks like. We'll scope a 4-week pilot and send a score report when it's done.
Or email directly: hello@reboo8.comTwenty years on the operations floor.
Annotation, evaluation, quality review — different names, same workflow. reb∞8 is what that workflow looks like when it ships as a product.
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 program — annotation, evaluation, moderation, customer support. The same workflow under different names.
When attention turned to AI training pipelines, the same workflow showed up. Thousands of contributors. Quality drifting before the benchmark could see it. The same fix kept getting rebuilt by hand.
5,000 contributors assessed. Infrastructure running. 14-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 same workflow that ran every program, now available as a product.
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 14-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 that runs every day.
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 — calibrated to your work.
Resume match, task benchmark, structured interview — calibrated to your rubric. Score determines who gets in.
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. Traced to person, session, and batch.
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, gated at every batch.
Before anyone touches a Tag task, they've cleared a task-specific Signal assessment. The quality loop starts before the first label is placed.
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. Ready to train on.
Badge Score drops mid-engagement → allocation drops automatically. Before the batch reaches your pipeline, while there's still time to correct it.
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.
What this work actually is.
Every preference ranking 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. The training signal is human judgment, made explicit.
Human judgment
at the hardest tasks
Preference ranking. Safety evaluation. Domain annotation. The tasks where a model can't evaluate its own output — and a person's judgment is the signal the training run depends on.
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
The Surcharge engine links earnings directly to your Badge Score. Improving accuracy increases what you earn — task by task, not by negotiation. Quality is the variable.
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.
Built for judgment work.
Accuracy holds when domain knowledge is real. We're looking for people who can evaluate AI output in fields they already know deeply — and who can hold that standard across task 5 and task 500.
Speed comes after accuracy. The Badge Score reflects how consistently you get the call right, not how many calls you make.
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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
Tell us your domain. We'll send the assessment.
Apply with your background and the domains you know. We review every application and reply within 48 hours with a task-specific Signal assessment.
Or reach us at support@reboo8.com