97%+ labeling accuracy · Scale to millions
Data Labeling Services
Your AI projects are stalled because you need thousands of images, text documents, or audio files labeled accurately - and your engineering team has better things to do. Acelerar’s trained labeling teams deliver production-ready datasets at scale with 97%+ accuracy and built-in quality controls.
Data Labeling
What are data labeling services?
Data labeling is the foundational step in building supervised machine learning models. It involves assigning meaningful tags, categories, or annotations to raw data so algorithms can learn to recognize patterns. Image labeling assigns class labels to images or regions within images. Text labeling tags entities, sentiments, intents, or categories in documents. Audio labeling marks speech segments, speaker identities, and sound events. Without accurately labeled data, even the most sophisticated ML architecture cannot learn. The challenge is producing consistent, high-quality labels at the scale modern models require - often hundreds of thousands or millions of labeled examples.
Market Data
The data management outsourcing market
Data management outsourcing is growing as businesses deal with increasing data volumes.
Labeling Types
Every labeling type your ML pipeline demands
Image & visual data labeling
Image classification, object detection labels, scene categorization, facial attribute tagging, medical image labeling, and satellite imagery classification. We label datasets for computer vision models across industries: retail (product recognition), automotive (road scene labeling), healthcare (radiology image classification), agriculture (crop disease identification), and manufacturing (defect detection). Every label follows your taxonomy with documented decision rules for edge cases.
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Text & document labeling
Sentiment classification, topic categorization, intent labeling for chatbots, content moderation labels, document type classification, and email routing labels. Our text labeling teams handle the nuance that automated pre-labeling misses: sarcasm in sentiment analysis, context-dependent intents, and domain-specific terminology that generic models misclassify. We label in 15+ languages for multilingual model training.
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Audio, video & multi-modal labeling
Speaker identification, speech-to-text labeling, music genre classification, sound event detection, video scene labeling, and activity recognition. For multi-modal datasets that combine image, text, and audio, we provide synchronized labels across modalities. Our audio labelers are trained on acoustic protocols and handle noisy environments, overlapping speakers, and varied accents that degrade automated transcription quality.
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Cost Savings
The real cost of in-house data labeling
A full-time data labeler in the US costs $38,000 to $48,000/year with benefits. With Acelerar, you get dedicated labeling teams that scale with your training data needs.
$42K/yr
per year / per person
Salary · Benefits · Labeling platform · QA overhead
$13K/yr
per year / per person
Protocol-trained · Consensus QA · Scales to millions
Why Outsource Data Labeling
Why ML teams outsource labeling to Acelerar
97%+ Labeling Accuracy
Every labeled dataset goes through consensus review: multiple labelers independently label the same samples, disagreements are resolved by senior reviewers, and accuracy is measured against gold-standard datasets.
Dedicated, Trained Teams
No generic crowdsourcing. Your project gets dedicated labelers trained on your specific taxonomy, guidelines, and domain. The same team works your project from start to finish for consistency.
Scale from Thousands to Millions
Start with a 5,000-sample pilot and scale to millions of labels. Our team capacity grows with your project - we ramp up additional trained labelers within one week without sacrificing quality.
70% Lower Cost Than US Teams
US-based labeling services charge $28-$45/hour per labeler. Acelerar delivers equivalent quality at 70% less, letting you label more data within the same budget and iterate on model training faster.
Tool Agnostic
Labelbox, Label Studio, CVAT, Prodigy, V7, Supervisely, or your custom platform - we work in whatever labeling tool your pipeline uses. No tool migration or integration overhead.
Structured Quality Reporting
Every delivery includes labeling accuracy metrics, inter-labeler agreement scores, edge case documentation, and label distribution analysis. You see exactly how your training data was produced.
How It Works
From raw data to labeled training sets in 5 steps
Scope
We review your labeling guidelines, taxonomy, data volume, and quality requirements. Unclear guidelines are clarified before any labeling begins.
Pilot
Labelers complete a pilot batch (200-500 samples). You evaluate quality, we incorporate feedback, and guidelines are refined until output meets your standard.
Label
Dedicated labelers process your full dataset following calibrated guidelines. Daily throughput and quality metrics are tracked and reported.
Review
Consensus review: overlapping labels compared, disagreements resolved by senior reviewers, accuracy measured against gold-standard samples.
Deliver
Labeled data exported in your required format with quality report: accuracy scores, label distribution, inter-labeler agreement, and edge case documentation.
ML models waiting on training data?
Send us your labeling guidelines and 500 sample data points. We’ll return a labeled pilot batch within one week so you can evaluate quality before committing.
Start a Pilot ProjectWe work with your data platforms
Our teams are trained on the platforms you already use.
What our data management clients say
“The Acelerar team is a self-sustaining machine. They’ve become an extension of our own team.”
“We needed reliable, fast data entry at scale. Acelerar delivered consistent quality from day one, no ramp-up time needed.”
“Acelerar handled our entire catalog migration (50,000+ SKUs) without a single missed deadline.”
Industry Outlook
Where data management outsourcing is heading
Data volumes are exploding, and outsourced data management teams are evolving with AI capabilities.