01
Crowdsourcing platforms give you volume, not quality. You post a task, random people pick it up, and you hope for the best.
The RL(T)HF Data Platform.
"Reinforcement Learning from Trained Human Feedback."
Publish your RLHF task. Trained labelers apply to work on it. Every labeler on Anoda AI has been prepared through gamified, task-specific learning — so you get informed, consistent preference data for your model.
A problem recognized at Microsoft, Mistral, Databricks, and every team training LLMs.
The H in RLHF is the weakest link.
02
Hiring and managing your own annotators is a full-time job you didn't sign up for. Training them is another one.
03
The real problem isn't bad labelers — it's untrained ones. Most platforms skip preparation entirely and sell you QA as the fix.
Anoda fixes quality at the source — the human.
Platform Features
Post a task. Trained labelers show up.
Publish
Task Marketplace
Post your RLHF task with your criteria, guidelines, and edge cases. Trained, qualified labelers on the platform apply to work on it — you approve and go.
Train
Gamified Labeler Training
Every labeler on Anoda learns through interactive, task-specific modules — examples, quizzes, calibration rounds. They prove competency before they can apply to your task.
Label
Preference Labeling Studio
Side-by-side comparison UI built for RLHF. Ranking, ratings, and structured reasoning — designed for the way preference data actually gets created.
Validate
Automated Quality Pipeline
Gold-standard checks, consensus scoring, anomaly detection. Trained labelers plus automated QA — bad labels don't ship.
Why teams switch to Anoda
Trained labelers, not random crowd
Every annotator on Anoda has completed gamified training and passed qualification gates. You're not posting into a void — you're hiring from a prepared workforce.
Post and go
Publish your task, set your criteria, approve applicants. Labelers are already trained on RLHF workflows — you don't manage onboarding, we do.
Gamified, not boring
PDFs and guideline docs don't work. Interactive learning with quizzes, examples, and calibration does. Labelers retain more, make fewer mistakes, ramp up 3x faster.
Self-serve from day one
Sign up, post your task, start getting data. No sales calls, no SOWs, no six-week enterprise onboarding.
Transparent pricing
Pay per validated label. Training and QA included — not an upsell. See costs before you commit.
Built for RLHF from day one
Not a generic annotation tool with a preference mode added later. Every feature exists because LLM training demands it.
From task to clean preference data
Step 01
Publish your task
Define your RLHF task — upload prompts, set criteria, specify edge cases. Push via API or use the dashboard.
Step 02
Trained labelers apply
Qualified annotators who've completed gamified training on your task type apply to work on it. You review and approve.
Step 03
Labelers evaluate
Approved annotators compare response pairs side by side. Which is better? Why? Structured reasoning captured with every choice.
Step 04
Export clean preference data
Validated pairs delivered in any format. Plug directly into your RLHF, DPO, or custom alignment pipeline via API.
“We've tried three crowdsourcing platforms. Every time it's the same — we post a task, fifty people apply, and we spend more time filtering bad labelers than actually getting data. Half the budget goes to QA and rework. There has to be a better way.”
— ML Lead, Stealth AI Startup
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Post the task. We'll send you trained humans.
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