Production-grade machine learning training data from Pune, India — 17+ Years Since 2008, 540+ in-house annotators, 810M+ images labeled. ISO 27001-Aligned, HIPAA-Aligned & GDPR-Aligned workflows. Bounding box, segmentation, LiDAR, NLP — every annotation type your AI pipeline needs.
Illustrative examples covering the full range of image labeling and tagging services we deliver — each type shown so you can see exactly what to expect.
Since 2008, Precise BPO has delivered AI training data services supporting computer vision, autonomous systems, medical imaging, NLP, and LiDAR pipelines — all from our Pune, India delivery centre operating 24/7 across global time zones. As a trusted data annotation outsourcing partner, we build every dataset to your exact model specification, label schema, and quality threshold.
Our annotators specialize in domain-specific labeling — applying bounding boxes, polygon segmentation, semantic masks, keypoint landmarks, 3D cuboids, and NLP tags for NER, intent, and RLHF. We support every major annotation platform including CVAT, Labelbox, Scale AI, Roboflow, and VGG Image Annotator — adapting to your toolchain and output schema without switching costs. This in-house bench keeps growing — see current annotator job openings if you'd like to join the team building these datasets.
For AI teams requiring high-volume annotation across computer vision verticals — autonomous driving, agriculture, retail, medical, and satellite imagery — we deliver pixel-level precision at scale. Our India-based annotation outsourcing model lets ML teams ramp from pilot to production without building in-house labeling infrastructure, reducing per-image costs by 40–60% against US or UK equivalents. Every delivered batch is production-grade ground truth ready to feed directly into your training pipeline.
Data annotation — also called data labeling or AI markup — is the process of adding structured metadata to raw data (images, video frames, text, audio, or point clouds) so that machine learning models can understand, classify, and reason about it accurately. Annotators apply bounding boxes, polygons, semantic masks, keypoints, text tags, and attribute labels to raw datasets, giving AI systems the ground truth they need to learn real-world patterns. A dedicated annotation workforce trained on your specific taxonomy is what separates reliable data labeling solutions from generic data processing.
It is the foundational technique behind object detection, semantic segmentation, pose estimation, autonomous driving perception, medical image analysis, and NLP model training across every major AI vertical. Unlike generic data processing, high-quality image annotation services and data annotation for AI require domain expertise — understanding visual context, edge cases, label taxonomy, and model-specific schema — to produce datasets that train accurate, production-ready AI systems.
Annotation outputs are structured as COCO JSON, YOLO TXT, Pascal VOC XML, CSV, or custom schemas — compatible with PyTorch, TensorFlow, YOLO, Detectron2, Labelbox, CVAT, Scale AI, and all major ML training platforms — designed to integrate directly into your pipeline without post-processing.
Precise BPO provides outsourced data annotation and labeled training datasets that power AI across autonomous vehicles, healthcare, agriculture, retail, satellite imagery, and enterprise NLP — enabling scalable global AI systems that depend on precise, consistent ground truth data.
Bounding boxes, semantic segmentation, LiDAR 3D cuboids, polyline lane marking, and pedestrian keypoints for self-driving perception stacks — handling camera, radar, and LiDAR fusion datasets at production volume.
HIPAA-aligned annotation for radiology scans, pathology slides, dermatology images, and surgical video — polygon segmentation of organs, tumors, and anatomical landmarks for diagnostic AI and surgical robotics models.
Crop detection, disease classification, yield estimation, and drone imagery segmentation for AgriTech AI — annotating satellite and UAV imagery with polygon and bounding box labels for precision farming models.
Product detection, visual search, catalog enrichment, and SKU attribute tagging across retail and fashion datasets — bounding boxes and attribute labels for recommendation engines, visual search tools, and inventory AI systems.
Building footprint detection, road network segmentation, land use classification, and change detection annotation on satellite, aerial, and GIS imagery — supporting mapping AI, urban planning, and environmental monitoring platforms.
NER, intent classification, sentiment analysis, span labeling, coreference resolution, and RLHF preference datasets for LLM fine-tuning, chatbot training, and enterprise NLP pipelines in 20+ languages.
Invoice extraction, KYC document classification, form field labeling, and handwriting recognition annotation — supporting OCR pipelines, fraud detection models, and intelligent document processing platforms under strict data controls.
Defect detection, component classification, robotic vision, and assembly-line quality control annotation — bounding boxes and segmentation masks on production imagery for predictive maintenance and visual inspection AI.
Most AI teams hit the same walls when scaling labeled datasets. Here's what we fix when you outsource data labeling to Precise BPO.
Crowdsourced or freelance work looks cheap — until your model's mAP tanks and you're paying engineers to re-label everything. Our human-in-the-loop QA system catches errors before delivery, not after. Every batch is independently reviewed before handover. Learn how we maintain 99.8% object detection accuracy at scale.
In-house teams hit capacity ceilings fast. Hiring, onboarding, and training specialists takes 4–6 weeks minimum — before a single label is created. We spin up a full project team within 48 hours. No hiring. No ramp-up lag. See our annotation process →
Medical imaging, financial documents, and defence projects require data to never leave controlled environments. Every member of our team is a permanent, NDA-signed, background-verified employee — no freelancers, ever. Encrypted SFTP, role-based access, VPN tunnels, and full audit logs. HIPAA-aligned service →
US and European labeling teams charge $25–$60/hr for work that costs $5–$9/hr at equivalent quality from our Pune centre. Our clients save 40–60% on data labeling costs vs in-house operations — without sacrificing accuracy, security, or turnaround time. Affordable data annotation outsourcing that doesn't cut corners. See transparent pricing →
Labeling guidelines drift when teams scale without proper rule enforcement. We assign dedicated project managers who maintain guideline consistency across every specialist, batch, and sprint — so your model training stays stable.
Models trained on clean data fail on real-world noise, occlusion, and rare classes. Our domain specialists — medical, automotive, agriculture — proactively identify and label edge cases that generalist vendors miss entirely.
🚀 Let us fix it — free. First 100 images labeled at no cost.
✓ No commitment ✓ Full NDA ✓ Delivered in 48 hours ✓ See quality before you spend a dollar
Start Annotation Project →Every image annotation and labeling service your ML pipeline needs — computer vision markup, video labeling, LiDAR, and NLP — with domain expertise, multi-level QA, and enterprise-grade security. Each engagement delivers ready-to-train labeled datasets in your required format.
Rectangular object marking for detection and classification. Foundation of computer vision pipelines at scale.
Explore bounding box →Pixel-level class masking for autonomous vehicles, medical image tagging, and satellite imagery. 99.2%+ accuracy.
View segmentation service →Precise shape outlines for irregular objects — crops, vehicles, body parts, architectural elements.
See polygon service →Lane detection, road markings, cable routing, linear infrastructure for autonomous systems.
View polyline service →Pose estimation, facial recognition, joint mapping. Skeletal keypoints for human action recognition.
Explore keypoints →3D point cloud markup for robotics, AVs, industrial automation. Full volumetric spatial tagging.
LiDAR & 3D cuboid →NER, intent classification, sentiment, span labeling, and RLHF data labeling for large language model training and fine-tuning.
Text & NLP services →Frame-by-frame multi-object tracking, action recognition, temporal event markup, and behavior analysis for surveillance, sports, autonomous driving, and media AI. Supports per-frame exports and interpolated tracks.
Enquire about video annotation →HIPAA-aligned PII redaction for medical records, financial documents, legal filings.
De-identification service →Lesion detection, organ segmentation, radiology AI. HIPAA-aligned clinical labeling workflows.
Medical imaging service →Full data annotation for autonomous vehicles: object detection, LiDAR, lane detection, and 3D cuboids for ADAS and self-driving perception stacks.
Automotive AI labeling →Crop monitoring, disease detection, drone imagery analysis for precision farming AI models.
AgriTech labeling →Garment tagging, attribute labeling, and visual search dataset creation for fashion AI and e-commerce recommendation engines.
Fashion AI labeling →Player tracking, ball detection, skeletal keypoints, and event labeling for sports analytics, coaching tools, and broadcast AI.
Sports AI labeling →Content classification and tagging for moderation AI pipelines. Sensitive material categorisation for platforms and trust & safety teams.
Content moderation →Native integration with every major annotation platform — your team keeps using the tools they know. We work within your environment and deliver in any format your ML pipeline requires.
Annotation type selection directly impacts model performance and labeling cost. This comparison helps computer vision and ML teams choose the right approach based on object shape, use case, and pipeline requirements. For a deeper breakdown, see our bounding box annotation guide. Once you know which annotation type fits your data, the next decision is who labels it — see how in-house, generic BPO, and Precise BPO compare further down this page.
| Criteria | Bounding Box | Polygon / Instance | Semantic Segmentation | Keypoint / Landmark |
|---|---|---|---|---|
| Shape | Rectangle — axis-aligned or rotated | Vertex-traced outline per object instance | Pixel-level mask per class | Coordinate points on joints or landmarks |
| Best for | Bounded objects — vehicles, people, products | Complex shapes — irregular or overlapping objects | Scene understanding — drivable area, background separation | Pose estimation, facial analysis, AR/VR datasets |
| Annotation Speed | Fastest — single drag per object | Moderate — vertex-by-vertex | Slowest — pixel-by-pixel fill | Fast — point placement per joint |
| Cost Efficiency | Highest — minimal effort per object | Medium — scales with shape complexity | Lowest — intensive per image | High — fast but requires domain expertise |
| Boundary Precision | Object-level (includes background) | High — shape-accurate | Pixel-perfect | Point-exact per joint |
| Video / Temporal | Excellent — fast frame-by-frame tracking | Good — higher effort per frame | Very high effort per frame | Excellent for pose tracking across frames |
| Common Use Cases | Retail, ADAS, medical, surveillance, drone imagery | Autonomous driving, agriculture, satellite, fashion | Autonomous vehicles, medical imaging, aerial mapping | Human activity recognition, sports analytics, AR/VR |
| Precise BPO Service | Bounding Box Annotation | Polygon Annotation | Semantic Segmentation | Landmark & Keypoint |
Not sure which annotation type fits your project? Talk to our data annotation specialists — we'll recommend the right approach based on your feature types, model architecture, and dataset requirements. For path and trajectory datasets, see our polyline annotation services as well.
Structured workflow covering requirement understanding, secure data ingestion, expert labeling, multi-stage QC, client review, and final delivery — optimized for 99.8% accuracy at scale.
Define annotation goals, object classes, labeling taxonomy, quality thresholds, and output schemas with your ML or computer vision team before any labeling begins — ensuring every annotator works to a single, locked specification.
Raw images, video, LiDAR, or text files are received via encrypted SFTP, normalized to standard formats, and structured into labeled batches under NDA-bound, ISO 27001-Aligned infrastructure — role-based access enforced from day one.
540+ in-house annotators label your data using client-specified tools or our internal platforms — bounding boxes, polygons, segmentation masks, keypoints, LiDAR cuboids, or text labels — with dedicated domain specialists for medical, automotive, agricultural, and NLP datasets.
Three-tier QC — automated consistency checks, independent annotator peer review, and senior reviewer sign-off — flags label errors before delivery. Every batch is verified against ground truth criteria to enforce 99.8% accuracy on every handover.
Annotated batches are submitted for your team's review. Feedback is incorporated via structured revision cycles — guidelines are updated and locked before the next batch begins to maintain consistency across your full dataset.
Outputs delivered in your required format — COCO JSON, YOLO, Pascal VOC, CSV, or custom schema — via secure transfer within agreed SLAs. Ongoing support for active learning pipelines, model retraining cycles, and extended annotation programmes.
Supplying AI training data and ML labeling for projects across mobility, robotics, agriculture, retail, healthcare, finance, GIS, security, media, manufacturing, and emerging domains — with specialist annotators assigned to each vertical.
High-quality labeled training data for self-driving and ADAS systems. Object marking, LiDAR, and semantic segmentation.
Automotive annotation →HIPAA-aligned medical image annotation — lesion detection, organ segmentation, radiology AI with precision tagging.
Healthcare AI annotation →AI models for crop monitoring, disease detection, and yield prediction from drone and satellite imagery.
Precision farming annotation →Product image tagging, catalog classification, and customer interaction data for recommendation engines and visual search.
Retail AI service →Precise ground truth datasets training robots for navigation, object recognition, and industrial automation with 3D cuboid labeling.
Robotics 3D annotation →Annotated financial documents for risk assessment, fraud detection, and automated claims processing.
Document de-identification →High-accuracy geospatial tagging for mapping AI, urban planning, and satellite imagery analysis.
Polygon & geospatial →Video and image tagging for threat recognition, behavior detection, and monitoring AI. Multi-object tracking.
Object detection labeling →Labeled datasets for video analysis, highlights generation, performance tracking, and visual search.
Sports annotation →AI training data for quality inspection, predictive maintenance, and smart factory automation.
LiDAR point cloud labeling →AI models for aerial, marine, and drone-based imaging. Terrain, vessel, and infrastructure detection from aerial tiles.
Polyline & aerial →AI initiatives in universities and research labs rely on our labeled datasets for experiments and model validation.
Get a Research Annotation Quote →Real-world examples showing how high-quality ground truth data and structured labeling improve AI accuracy, automation, and decision-making across industries globally — from teams that outsourced image annotation to Precise BPO.
High-accuracy object detection labels and pixel-level masks across thousands of frames. Autonomous vehicle labeling →
Dense pixel-mask segmentation of lane boundaries, curbs, and road infrastructure for AV path planning. Automotive labeling →
Product image tagging and attribute labeling across 500K+ products. Retail annotation →
Binary masks highlighting damaged areas on product photos to automate quality control. Retail AI labeling →
3D cuboids and keypoints labeled for precise grasp points across varied object types and orientations. 3D cuboid service →
Text regions annotated and NER tagging applied for key entities across large document volumes. Text & NLP tagging →
Polygon segmentation applied to satellite tiles for precise feature extraction at city scale. Satellite polygon segmentation →
Multi-keypoint skeleton markup for each player and the ball across match footage. Player tracking annotation →
Dense defect segmentation on production line images to train automated inspection AI. Defect segmentation service →
Rectangular annotation with activity tagging for human and object detection across surveillance footage. Bounding box service →
Polygons with class segmentation for land-use analysis across drone-captured tiles. Polygon annotation →
Object tracking with mask tagging for precise vessel detection on water. Semantic segmentation →
Content tagging and classification for automated moderation pipelines. Explicit content tagging →
Caption tagging with region markup across images and associated text for multimodal pipelines. NLP & Text Annotation →
Free-form text tagged for NLP pipelines including intent detection, entity recognition, and sentiment analysis. Text & NLP labeling →
Polygonal crop boundaries with multi-class segmentation across large-scale farm imagery. Agriculture annotation →
Polygon and pixel-level tagging reviewed by QA specialists, HIPAA-aligned throughout. Diagnostic imaging service →
Instance segmentation of exterior damages enabling end-to-end automated claims assessment. Damage segmentation service →
Every labeling type maintains dedicated quality benchmarks tracked project-by-project. We publish our accuracy benchmarks — because our clients need to trust the data that trains their models.
3-Tier QA Pipeline with 0.92+ Cohen's Kappa minimum. Since 2008, refined across 810M+ images.
First-pass labeling with embedded rule checks. Self-review against guideline checklist before handoff.
Blind cross-review by a separate specialist. IoU checks, pixel-diff scoring, inter-annotator agreement measured per batch.
Random 10% of every batch re-reviewed by a senior specialist. Drift detected and flagged before delivery.
Trusted by 700+ Enterprises, Research Labs & AI Startups
Across 27 countries
References available on request.
Three realistic paths for building AI training data — compared across the criteria that determine whether your model ships on time, on budget, and at production quality.
| Criteria | In-House Team | Generic BPO | ✦ Precise BPO Recommended |
|---|---|---|---|
| Time to first label | 4–8 weeks | 1–2 weeks | 48 hours |
| Cost vs in-house | Baseline (highest) | Lower, but markups apply | 40–60% savings |
| AI domain expertise | Depends on hiring | Generalist only | 540+ AI specialists |
| Multi-level QA | Varies by team maturity | Inconsistent | 3-tier QA, 99.8%+ |
| Scale-up speed | Months (hiring cycles) | Days–weeks | Days, 540+ ready |
| Security alignment | Varies | Verify carefully | ISO 27001 · HIPAA · GDPR |
| Freelancer risk | None (in-house) | Common practice | Zero freelancers ever |
| Free pilot | No | Rarely | 100 images free |
| Dedicated PM | Internal only | Shared resource | Dedicated from Day 1 |
Ready to make the switch? Start with a free 100-image pilot — no commitment, full NDA, delivered in 48 hours.
🚀 Start Free Annotation PilotTrusted by 700+ enterprises, research labs, and AI startups across 27 countries for consistent, high-accuracy annotation work.
Their object detection labeling quality exceeded our internal benchmarks. The team understood our class hierarchy complexity from day one, and their QA process caught edge cases our own reviewers missed. We've scaled from a 10K image pilot to 2M+ frames with zero quality degradation.
We needed HIPAA-aligned medical imaging labeling — not just any vendor claiming compliance. The team provided documentation, signed BAA, and their annotators demonstrably understood the clinical context. Recall improved by 18% within the first 3 months of retraining.
After trying two crowdsourcing platforms with inconsistent results, switching to this partner was transformative. Their polygon annotations for our crop segmentation model were remarkably consistent across 3M+ images. Project manager was available across time zones — genuinely enterprise-grade service.
Precise BPO is an India-based AI data annotation company with 17+ years of experience since 2008 — delivering accurate, scalable data labeling, and cost-efficient labeling services to computer vision and ML teams worldwide. Teams that outsource data annotation to us get production-grade bounding boxes, segmentation, LiDAR, NLP, and video labeling — handled by 540+ in-house annotators. As a data annotation vendor built for scale, we support data labeling for autonomous vehicles, annotation for healthcare, retail, and agriculture AI alike. Trusted across US, UK, Canada, Australia, Europe, Middle East, APAC & LATAM.
Start Your Free Data Annotation PilotDeep institutional knowledge of annotation workflows — from simple bounding boxes to complex polygon segmentation, LiDAR point clouds, and multi-modal NLP datasets — refined over nearly two decades.
Dedicated, background-verified, NDA-signed annotation specialists delivering precise AI training labels at enterprise scale — zero freelancers, zero crowdsourced workers, zero quality compromise.
Encrypted SFTP, role-based access controls, VPN tunnels, and full audit logs protect your datasets end to end. BAA signing available for HIPAA-scoped medical imaging annotation projects.
3-tier QA pipeline combining annotator self-check, automated geometry validation (0.92+ Cohen's Kappa minimum), and expert audit — ensuring every labeled batch meets your model's exacting quality threshold.
India-based delivery at a fraction of Western BPO or in-house costs — no recruitment overhead, no tooling investment, no long contracts. Free pilot batch before any financial commitment.
We annotate within your preferred tooling — CVAT, Labelbox, V7, Scale AI, SuperAnnotate — and deliver in COCO, YOLO, Pascal VOC, or any client-defined schema for seamless ML pipeline integration.
Transparent per-item and hourly rates. No hidden fees. Volume discounts from 50K+ items.
| Markup Type | Rate Range (USD) | Best For |
|---|---|---|
| Bounding Box | $0.01 – $0.06 | Object detection, simple images |
| Polygon / Instance | $0.08 – $0.35 | Precise shape outlines, irregular objects |
| 3D Cuboid / LiDAR | $0.12 – $0.50 | Robotics, AV depth data |
| Segmentation | $0.10 – $0.45 | Autonomous driving, medical imaging |
| Polyline | $0.06 – $0.25 | Lane detection, road markings |
| Landmark / Keypoint | $0.015 – $0.05 | Pose estimation, facial recognition |
| De-identification | $0.015 – $0.05 | Medical records, HIPAA alignment |
| Team Type | Rate Range | Best For |
|---|---|---|
| Annotation Specialist | $5 – $7 / hr | High-volume standard annotation |
| Senior / Domain Specialist | $7 – $9 / hr | Medical, LiDAR, complex segmentation |
| QA Reviewer | $6 – $8 / hr | Independent validation and auditing |
Hourly model suits dedicated team engagements, ongoing programs, or mixed tagging types. Minimum 160 hrs/month. NDA, IP rights, and SLA included.
Precise BPO is a dedicated data labeling company in India that has partnered with 700+ global clients to build the machine learning training data their models depend on — accurately, at scale, and within budget. As an enterprise data labeling partner, we deliver consistent quality whether you're a startup running your first pilot or a large team replacing an existing data annotation vendor. Enterprise quality, transparent pricing, zero freelancers.
Our India-based data labeling delivery hub runs 24/7 across time zones — covering US, UK, EU, APAC, Middle East, Australia, Canada, and LATAM with region-specific annotation standards and compliance protocols.
Clear answers on data annotation scope, accuracy controls, output formats, large-scale project management, tool compatibility, security compliance, and pricing.
Data annotation is the process of labeling raw data — images, video, audio, text, or 3D point clouds — so AI and machine learning models can learn from it. Labels tell the model what objects, patterns, or meanings exist in the data. Without accurately annotated training datasets, AI models cannot achieve reliable performance in production. High-quality annotation directly determines model accuracy, edge-case handling, and the speed at which models reach production readiness. Data labeling services for machine learning are foundational — no labeled data means no learning. See our complete guide to data labeling for a deeper overview.
Precise BPO supports the full spectrum of data annotation tasks: bounding box annotation, semantic segmentation, instance segmentation, polygon and polyline labeling, landmark and keypoint annotation, 3D LiDAR point cloud labeling, video object tracking, text classification, NER (Named Entity Recognition), sentiment tagging, and audio transcription. We handle image, video, text, and audio modalities across computer vision, NLP, and speech AI use cases — all under one engagement. Teams that also need structured data management can combine annotation with our data entry outsourcing services, or migrate legacy records first through our data conversion services.
We operate a 3-tier quality control pipeline on every project. Tier 1 involves primary annotators working from client-approved labeling guidelines and ontologies. Tier 2 is an in-house QA review layer that checks class accuracy, boundary precision, and consistency against gold-standard samples. Tier 3 is a senior review stage for edge cases and class ambiguity resolution. This human-in-the-loop annotation process delivers a 99.8% accuracy rate across bounding box, segmentation, keypoint, and tracking tasks — validated on every batch before delivery.
Enterprise annotation datasets are managed through standardized labeling guidelines, batch-based workflows, and structured review cycles. Each project starts with a detailed ontology and annotation schema aligned to your model's class requirements. Work is segmented into manageable batches while maintaining consistent class definitions, boundary standards, and inter-annotator agreement metrics. This allows teams to scale volume, update datasets for active learning cycles, and support long-term model retraining without annotation drift or label inconsistency across versions.
Data annotation is fundamental across autonomous vehicles (LiDAR, camera fusion), healthcare AI (medical imaging, radiology), retail and e-commerce (product detection, shelf analytics), agriculture (crop and disease detection), robotics (object manipulation and scene understanding), surveillance and security, sports analytics, and natural language processing products. Any industry deploying computer vision, NLP, or speech AI requires high-volume, accurately labeled training data. If you're evaluating providers, our data annotation company comparison guide covers what to look for when shortlisting partners.
Consistency is enforced through predefined annotation guidelines, class taxonomy documentation, inter-annotator agreement (IAA) scoring, and gold-standard calibration sets. Before any production batch begins, annotators complete calibration tasks scored against gold data to verify their understanding of edge cases and class boundaries. QA reviewers flag annotation drift, class ambiguity, and boundary inconsistencies in real time. This framework ensures labels remain coherent across large teams, long timelines, and multiple model retraining cycles. See our annotation governance framework for full details.
Data annotation outputs are delivered in the format your ML pipeline requires: COCO JSON, PASCAL VOC XML, YOLO TXT, LabelMe JSON, CSV, and custom schemas. For video, we deliver MOT-format tracking files or frame-by-frame JSON. For NLP, outputs include CoNLL for NER, JSON for classification, and custom formats for intent/slot labeling. All deliverables integrate directly with PyTorch, TensorFlow, Roboflow, and major cloud ML services.
Pricing depends on dataset volume, annotation type complexity, class density per image or document, QA depth, and turnaround requirements. Common engagement models include per-image, per-object, per-frame, hourly, and monthly retainer. Our India-based delivery typically offers 50–60% savings versus US, UK, or Australian in-house teams. All engagements include a free pilot batch before any financial commitment. See our data labeling pricing guide or request a tailored quote.
Yes. Our data annotation workflows are ISO 27001-Aligned, HIPAA-Aligned, and GDPR-Aligned to protect sensitive training datasets for global AI enterprises. Every annotator signs an NDA before project access is granted. Roles are permission-scoped so annotators only access the specific dataset segments assigned to them. Automated security audits run continuously across all project environments, and data transfer protocols follow enterprise-grade encryption standards — ensuring your proprietary training data and model IP remain fully protected end to end.
Practical guides on data labeling, annotation pricing, vendor selection, and structured data entry — for AI engineers and ML teams evaluating annotation partners.
