Data Annotation Services
DataForce accelerates your data labeling processes with our range of human-annotation services at scale
How it Works
Annotation adds meaningful labels to data so that they can be used as a means of learning by various systems. It is pivotal that the data is structured in the right way to make it useable for machine learning. There are many different annotation task types, depending on project needs.
We can annotate anything.
- Image
- Video
- Text
- Audio
Image Annotation
Empower your computer vision model and automate your image-based data labeling. DataForce’s image annotation services will meet your project’s needs, including bounding boxes, semantic segmentation, instance segmentation, polygons, image classification, and more.
Video Annotation
Create highly accurate models with labeled video data sets. We provide video classification and annotation services, including LIDAR, object detection, video tracking, and more.
Text Annotation
Backed by the world’s largest provider of language solutions, TransPerfect, DataForce leverages its global database of linguistic experts to provide services in natural language processing (NLP), morphosyntactic annotation, named entity annotation, and more to ensure your model is equipped with high-quality text classification and annotation.
Audio Annotation
Enrich voice-enabled applications with automatic segmentation and audio annotation. Make the most of your speech interactions using our qualified global linguist database. Train your model with high-quality audio transcripts in a variety of languages.
Success Stories.
We've partnered with thousands of companies around the world. View a selection of our customer stories below to learn more about how DataForce has helped clients excel in the global marketplace.
Frequently Asked Questions
Have questions? We're here to help. Here are some of our most common queries.
Answer: While standard NLP handles basic sentiment and entity tagging, LLM development requires highly nuanced, semantic annotation. DataForce provides advanced annotation workflows tailored for generative models, including:
- Supervised Fine-Tuning (SFT) Data Generation: Creating high-quality prompt-and-response pairs across various domains (coding, creative writing, factual retrieval).
- Multi-Turn Dialogue Labeling: Annotating long-form conversational threads to teach models context retention, user intent tracking, and conversational flow.
- Preference Labeling & Ranking: Scoring model outputs based on custom rubrics (e.g., helpfulness, truthfulness, conciseness) to feed into RLHF (Reinforcement Learning from Human Feedback) pipelines.
- Safety and Constraint Tagging: Labeling data to teach models how to handle negative constraints (e.g., "Do not mention X") or identifying harmful content for guardrail training.
Answer: General crowdsourcing fails when an LLM needs to learn complex Python coding, medical diagnostics, or financial analysis. DataForce solves this by leveraging TransPerfect’s global network to recruit, test, and vet Subject Matter Experts (SMEs).
We segment our workforce by domain expertise. If you are training a code-generation model, your data is annotated by verified software engineers. If you are building a legal assistant, we deploy annotators with legal backgrounds. Every contributor undergoes strict baseline testing and continuous spot-checking to ensure that the logic, factual accuracy, and alignment of their annotations meet rigorous enterprise standards.
Answer: LLM annotation guidelines can often be dozens of pages long and highly subjective. To ensure inter-annotator agreement (IAA) and data consistency, DataForce utilizes a strict programmatic framework:
- Gold Standard Baseline Testing: Before entering production, annotators must pass calibration tests using pre-vetted "gold standard" datasets.
- Real-time Adjudication & Consensus: We deploy multi-reviewer workflows (e.g., 2+1 blind reviews) where multiple annotators grade the same data point, and a senior linguist or SME adjudicates discrepancies.
- Dynamic Guideline Updates: As edge cases emerge, our project managers update the annotation rubrics globally and push real-time alerts to the platform, minimizing drift over long-term projects.
Answer: Absolutely. As LLMs evolve into multi-modal foundation models, our annotation services adapt seamlessly. We support complex, cross-modal labeling pipelines, such as:
- Image-to-Text & Video-to-Text: Writing highly detailed, dense descriptive captions for visual datasets to train vision-language models (VLMs).
- Audio-to-Text Alignment: Time-stamping and annotating emotional tone, background noise, and speaker shifts in acoustic data for speech-to-text and voice-generative models.
- Interleaved Grounding: Labeling specific regions of an image or timestamps in a video that correspond directly to clauses in a text prompt, helping your model learn true multi-modal context.
DataForce Benefits.
Scalable
Onboard up to 1000 annotators within weeks of project kickoff, leveraging a global community of over 1.3 million contributors.
Secure
Your data is secure with SSAE 16 SOC 2, ISO 27001, HIPAA & GDPR.
Seamless
Integrate directly with the DataForce annotation platform—your own or a third-party solution.
Let's work together!
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