A model is only as reliable as the labels it learns from. Annotera turns raw, unstructured data into consistently labeled training, validation and test sets, so ML teams spend their time on models instead of managing labelers and repairing datasets.
Our data annotation services are used by computer vision, NLP, speech and generative AI teams at startups and enterprises, across autonomous vehicles, healthcare, retail, financial services and robotics. Every program runs with dedicated, employed annotators trained on your guidelines and scored against your gold set, not an anonymous crowd. If you are new to the topic, our guide to what data annotation is explains the main types, the process and how quality is measured.
Annotera is part of Fusion CX, with 17,000 people across 40 delivery centers in 13 countries. Teams that want a fully managed, dedicated operation can use our data annotation outsourcing services, with onshore, nearshore and offshore delivery.
No two AI projects need the same labels. Choose a single annotation service or combine several in one program; each page below covers the methods, use cases and QA approach for that data type. Specialist work includes LiDAR and 3D point cloud annotation, speech transcription, content moderation and named entity recognition.
QA Layers
Annotator, lead and independent review
Delivery Centers
Across 13 countries
Languages
Text and audio annotation
Free Pilot
On your own data
*Delivery centers and countries refer to the Fusion CX delivery network.
Every engagement follows the same sequence, so quality is proven before volume arrives and stays visible after it does.
We review your data, label set and guidelines, then annotate a sample, usually within 48 hours, and score it against your gold set and acceptance criteria.
Edge cases, accuracy thresholds and turnaround are agreed in writing. Annotators are trained and certified on your guidelines before production starts.
Batches ship with quality reports. A named delivery manager tracks accuracy and throughput every week, and the team grows or shrinks with your volume.
Our AI data annotation services combine model-assisted labeling with human judgment. Where a model is reliable, it pre-labels routine items such as common objects, steady video motion or clear speech, and trained annotators review and correct every suggestion. Low-confidence and high-risk items go straight to senior annotators, and gold sets are labeled without pre-labels so accuracy is always measured against the truth, not against the model.
The result is faster turnaround on routine work without the quality loss that comes from annotators simply accepting what a model proposes. We can use your own models for pre-labeling, which focuses human effort on exactly the errors your model makes. Our guide to pre-labeling and AI-assisted annotation explains how to measure whether it is working.
Labeling at scale only works if quality, security and continuity hold up after the pilot. These are the commitments behind every program.
Employed annotators assigned to your project and trained on your domain, from clinical text to LiDAR scenes, so knowledge builds instead of resetting with every batch.
Annotator self-review, team-lead checks and independent QA validation, scored against gold sets with accuracy reported per class and per error type.
Operations certified to ISO/IEC 27001:2022, SOC 2 Type II, HITRUST CSF and PCI DSS 4.0.1, with HIPAA-compliant workflows and role-based access.
Onshore, nearshore and offshore teams, or a blend of all three. See our data annotation outsourcing models.
We work inside your annotation platform or in established commercial tools, and deliver in the formats your training pipeline expects.
A free 48-hour pilot on your own data, so you judge quality on evidence before signing anything.
Annotation teams are organized by domain, so the people labeling your data understand the context behind it.
Object, lane and LiDAR labeling for perception, ADAS and autonomous delivery programs.
Medical imaging, clinical text and clinical audio annotation in HIPAA-compliant workflows.
Shelf monitoring, product recognition and cashierless checkout data at SKU level.
Document extraction, entity recognition and transaction data for risk and fraud models.
Contract entity recognition, clause classification and document review datasets.
Video analytics, person and vehicle tracking, and event detection.
Crop, livestock and aerial imagery labeling for yield, health and monitoring models.
Egocentric video, teleoperation and multi-sensor data for robot learning.
Annotation and human-in-the-loop programs running today for retail, public safety and autonomous delivery clients.
Autonomous retail
Annotation partner since 2020 to a global cashierless-store technology company, delivered from India, the Philippines and Albania for stores and live-event venues across the U.S. and Europe.
Wildfire monitoring
A 100+ person human-in-the-loop team went live on April 10, 2025, met every KPI by week four and reached 100% certification on real wildfire scenario simulations.
Autonomous delivery fleet
A Watcher and Resolver workforce supported a global ride-sharing leader’s autonomous delivery operations: 656,000+ sessions, zero accidents and 3x capacity growth in under six months.
Straight answers on scope, quality, pricing, security and how to get started.
Data annotation services provide the trained people, processes and tools to label your raw data (images, video, text, audio, LiDAR or LLM outputs) so machine learning models can learn from it. A managed provider such as Annotera refines the guidelines with you, labels the data, runs quality assurance and reports accuracy against agreed targets.
Text, images, video, audio, LiDAR and 3D point clouds, and LLM and generative AI data such as prompts, responses and preference rankings. We also collect new training data when you do not have enough of your own, and multilingual work is done by native speakers.
Every program uses written guidelines, a gold set and three-layer QA: annotator self-review, team-lead checks and independent QA validation. Accuracy is reported per class and per error type, not as one overall score, and recurring errors are fed back into the guidelines.
Pricing depends on the data type, the annotation method, the expertise required, quality targets and volume; simple image classification is priced very differently from 3D LiDAR or clinical text. Most programs are priced per unit (per image, minute of audio or record) or per dedicated annotator. A pilot on your own data gives an accurate quote before you commit.
A pilot on a sample of your data is usually delivered within 48 hours of receiving the data and guidelines. Production ramp time depends on team size and how specialized the work is, and is agreed with you during the pilot.
Yes. Work runs inside Fusion CX operations certified to ISO/IEC 27001:2022, SOC 2 Type II, HITRUST CSF and PCI DSS 4.0.1, with HIPAA-compliant workflows and role-based access. We work only with client-provided data and never resell datasets.
Yes. We work in your in-house or commercial annotation platform, or provide established tools if you do not have one, and deliver in the export formats your training pipeline expects.
Data annotation services can be anything from a one-off labeling project to a long-running program. Data annotation outsourcing usually means handing the whole labeling operation to a dedicated external team over the long term. Annotera supports both; see our data annotation outsourcing services for the managed model.
