Support FAQs

Annotated data is delivered in the format you need — COCO, Pascal VOC, JSON, CoNLL, PCD, or a custom schema — along with a full quality report.

Unidata uses established commercial and open-source annotation platforms - e.g. CVAT, Label Studio, plus format-specific tools (LabelImg, V7, RectLabel, VoTT, Prodigy for images/video; Audacity, Sonix, Descript, Speechmatics for audio; CloudCompare, 3D Slicer, SUSTechPOINTS, Voxel51 for 3D/LiDAR) - combined with AI-powered automation and human review.

Data labeling covers basic tasks: assigning a category or tag to a whole item, such as image- or video-level classification. Data annotation is broader. It includes semantic segmentation, keypoint and landmark marking, polygon and cuboid outlines, temporal and event marking, metadata enrichment, and relationship mapping — the level of detail advanced ML models such as object detection or pose estimation need.

Image: bounding boxes, polygons, semantic/instance segmentation, keypoints, 3D cuboids, classification, landmarks, line/mask annotation. Video: object tracking, semantic/instance segmentation, action recognition, keypoint & event tracking, temporal segmentation, polylines, 3D cuboids, scene-text recognition. Audio: speech-to-text transcription, speaker diarization, sound-event detection, emotion recognition, music tagging, voice-activity detection, phoneme-level and utterance classification. 3D / Point Cloud / LiDAR: 3D bounding boxes, semantic/instance segmentation, object tracking, keypoints, mesh & volume annotation, lane/road-marking and terrain annotation.

Yes. Unidata runs projects on a Scrum-based Agile model, with iterative delivery, fast feedback loops, and flexible scaling as requirements evolve.

Timelines depend on data type, dataset size, and annotation complexity, so Unidata evaluates each project individually and provides a clear delivery schedule up front. A team of 1,000+ trained annotators, combined with AI-assisted tooling, keeps turnaround fast without compromising quality.

Each project follows a structured, milestone-based workflow: (1) kickoff briefing and task setup, (2) NDA, (3) pilot and scoping estimate, (4) tooling and workflow configuration, (5) execution by domain-matched annotators, (6) human-in-the-loop QA review, and (7) delivery of a production-ready dataset with a full quality report. Every project is supervised by a dedicated project manager.

Yes. Unidata scales human annotators, automation tools, and QA workflows to meet enterprise-level requirements across diverse datasets and 19+ industries.

Your data is handled exclusively by Unidata's managed team of 1,000+ experienced annotators with domain expertise across 19+ industries. It is never outsourced to open crowdsourcing platforms.

Every batch goes through a multi-stage QA process that combines human review with automated, AI-assisted validation. Unidata's dedicated Quality Control Department, with 6+ years of experience, reviews annotated data daily, tracks metrics such as error rate, inter-annotator agreement (IAA), and intersection over union (IoU) for spatial annotations, and benchmarks results against curated "golden" reference samples.

Unidata delivers 95%+ annotation accuracy, validated daily by the Quality Control Department. Exact accuracy targets are agreed for each project's data type and requirements before annotation begins.

Yes. Every engagement can start with a small, representative pilot batch with a clear cost estimate, so you can validate annotation quality, workflow, and compatibility with your ML pipeline before scaling to full production volume.

There is no strict minimum. Unidata supports both small pilots and large production-scale projects. Pilot batches are usually 10–100 samples, depending on task complexity; typical engagements start at 500–5,000 data points (images, video clips, audio files, etc.), and a full training dataset is commonly 5,000–50,000.

Yes. Datasets can be integrated with complementary regional or modality datasets to improve model generalization (for example, combining license-plate datasets from multiple countries for multilingual OCR).

Yes. Datasets are delivered in widely supported file formats (e.g. JPG, PNG, MP4/MOV, WAV/MP3, CSV/JSON/XML), which integrate directly with standard computer-vision and machine-learning frameworks.

After a request is submitted, the Unidata team contacts the client to confirm requirements and prepares the necessary agreement/documentation. Once the agreement is signed and payment is completed, the dataset is delivered securely (typically via cloud access) within 3–10 business days.

All datasets are securely hosted on AWS cloud infrastructure. Storage and data-management practices comply with ISO 27001 and ISO 27701 standards, ensuring high availability, scalability, and strong information-security/privacy protection.

Yes. All datasets are curated in full compliance with GDPR and other applicable data protection laws; data is collected only from legally permissible sources, and synthetic datasets contain no real personal information at all.

Yes — Unidata datasets are proprietary and collected specifically for its clients; they are not scraped from open-source repositories or otherwise publicly available.

Unidata offers both: most biometric, speech, and traffic datasets are real-world recordings from real participants or environments, while certain identity-document datasets (e.g. synthetic passports, driver's licenses) are fully AI-generated and contain no real personal data. Each product page states which type applies.

Depending on the dataset, data is collected via vetted crowdsourcing platforms, controlled in-studio recording sessions, verified data-collection partners, or by parsing real-world footage/imagery; synthetic datasets are generated with AI. In every case the sources are legally permissible and ethically sourced.

Unidata uses a dual-licensing model: a free sample is available for trial/evaluation, while access to the complete dataset is granted only after purchase.

Yes. Unidata can build a custom dataset tailored to a client's requirements — specific demographics, geographies, languages, attack/scenario types, annotation formats, volume, or recording conditions — as an alternative or supplement to the off-the-shelf datasets.

Confirm the file formats, annotation/metadata types, dataset size, and demographic or scenario diversity match your project's needs, and request the free sample first to validate quality and compatibility with your pipeline.

Yes. Unidata provides a free sample of virtually every dataset so you can evaluate data quality, file formats, and annotation/metadata structure before committing to a full purchase.