⚡ Enterprise Data Annotation Company
Piezee delivers accurate, fast, and cost-efficient data annotation services — image, video, text, and audio labeling — backed by dedicated project managers and multi-layer quality assurance. Built for AI startups and enterprise ML teams that can't afford to compromise on training data quality.
What We Do
From raw images to production-ready labeled datasets, Piezee covers every annotation type your machine learning pipeline requires — with precision and scale.
Precision image labeling including bounding boxes, polygon annotation, key point detection, and landmark annotation for computer vision and object recognition models. We support JPEG, PNG, TIFF, WebP, and RAW formats with customizable label taxonomies.
Frame-by-frame video annotation and temporal tracking for action recognition, scene segmentation, and object tracking. Our annotators handle everything from surveillance footage to sports analytics and autonomous driving video streams.
Named entity recognition (NER), intent classification, sentiment labeling, relation extraction, and custom text tagging for NLP model training. Available in 30+ languages with domain-specialized annotators in legal, medical, and financial verticals.
Speech transcription, speaker diarization, audio event tagging, emotion labeling, and phoneme-level annotation for speech recognition, voice AI, and audio classification systems. High accuracy across accents and noisy environments.
Tight-fitting bounding boxes and precise polygon annotations for object detection and instance segmentation. Our annotators maintain pixel-level precision critical for autonomous vehicle perception and medical imaging AI.
Pixel-wise semantic segmentation for scene understanding in autonomous driving, satellite imagery, and medical scans. We deliver panoptic, semantic, and instance segmentation maps with configurable class hierarchies.
End-to-end NLP dataset creation — from data collection and cleaning to multi-label classification and entity tagging. We support RLHF (Reinforcement Learning from Human Feedback) annotation workflows for LLM fine-tuning projects.
Every AI application is unique. Piezee builds custom annotation pipelines with bespoke ontologies, specialized tooling, and domain-specific annotator training for projects that don't fit standard templates. Tell us your use case — we'll engineer the workflow.
Industries We Serve
Our annotators are trained in industry-specific terminology, object types, and edge cases — so your training data reflects real-world complexity.
Whether you're training a medical imaging classifier, building a retail product detector, or improving the perception stack of an autonomous vehicle, Piezee provides the labeled data precision your model depends on.
Why Piezee
When your model performance depends on the quality of your training data, you need more than a crowd-sourcing platform. You need Piezee.
01
Every project passes through a structured QA pipeline: annotator → peer reviewer → senior QA analyst → project manager. We measure inter-annotator agreement (IAA) and only ship batches that clear our accuracy threshold — typically 98.5% or higher.
02
You get a single point of contact who understands your ML goals, communicates proactively, and manages the annotation team on your behalf. No platform tickets, no anonymous support — direct access to the people doing the work.
03
From 2 annotators for a quick pilot to 200+ for a production ramp, Piezee's managed workforce scales with your data volume. We recruit, train, and manage annotators so your engineering team stays focused on model development.
04
Enterprise-grade data security as standard. All annotators sign strict NDAs. Data is transferred over encrypted channels. Role-based access controls ensure only authorized personnel touch your assets. GDPR-compliant workflows available.
05
Outsourcing data annotation to Piezee reduces cost by 60–80% compared to building an in-house labeling team — without sacrificing quality. No HR overhead, no tool licenses, no management complexity. Pay per asset or per project.
06
We set realistic SLAs and meet them. Pilot projects in as little as 48 hours. Large-scale production batches delivered on agreed milestones. Real-time progress dashboards keep you informed without requiring daily check-ins.
Our Process
A clear, repeatable five-stage process that eliminates guesswork and gives your team full visibility from kickoff to delivery.
We deep-dive into your annotation task, ontology, edge cases, and ML objectives to build a precise annotation guideline.
A small sample batch (usually 200–500 assets) is annotated, reviewed together, and calibrated to meet your quality bar before full production begins.
Production annotation begins with your dedicated team, following the calibrated guidelines and hitting agreed throughput targets.
Every batch is reviewed through our multi-layer QA process. Failed assets are reannotated before batch approval and delivery.
Approved datasets are delivered in your preferred format (COCO, Pascal VOC, YOLO, JSON, CSV, custom) via secure transfer.
Technology & Tools
We work with your preferred annotation tooling or recommend the right platform for your project type and scale.
Don't see your preferred platform? Piezee annotators are cross-trained on multiple tools and can adapt to proprietary or custom annotation interfaces within your existing infrastructure. We integrate into your workflow — not the other way around.
Data Security
We understand that your training data is proprietary. Whether it contains sensitive medical images, confidential business documents, or competitive product datasets, Piezee treats data security as a non-negotiable baseline — not an optional upgrade.
Piezee's security posture is designed to satisfy enterprise procurement and legal requirements. We support custom DPAs (Data Processing Agreements), security questionnaires, and third-party audits.
For highly sensitive projects — including healthcare, government, or financial services data — we offer isolated annotation environments where internet access is restricted and all tools are audited.
Discuss Security Requirements →Results We've Delivered
Real outcomes from real annotation engagements across AI verticals.
Autonomous Vehicles
A US-based autonomous driving company needed 500,000 LiDAR point cloud frames annotated with 3D bounding boxes alongside synchronized camera image polygon annotations across 12 object classes including pedestrians, cyclists, and vehicles in adverse weather conditions.
Healthcare AI
A healthtech company developing AI-assisted radiology tools required pixel-level semantic segmentation of 80,000 chest X-rays and CT scan slices. Piezee assembled a specialist team trained in radiological terminology and worked under a HIPAA-compliant data agreement.
NLP / LLM
A foundation model startup needed high-quality preference data for reinforcement learning from human feedback. Piezee's specialist writing team generated 25,000 prompt-response pairs with comparative rankings across helpfulness, harmlessness, and factual accuracy dimensions.
FAQ
Everything AI and ML teams ask before choosing a data annotation partner.
Piezee offers a full spectrum of AI data annotation and machine learning data labeling services, including image annotation, video annotation, text annotation, audio labeling, bounding box annotation, polygon annotation, semantic segmentation, NLP labeling, and custom annotation projects tailored to your specific ML pipeline and data schema.
Piezee applies a structured multi-layer quality assurance workflow on every project. Each batch goes through individual annotation, peer review, senior QA analyst review, and final project manager sign-off. We measure inter-annotator agreement (IAA) throughout and only ship batches that meet or exceed the agreed accuracy threshold — typically 98.5% or higher. Failed assets are reannotated before delivery, never skipped.
Yes. Data security is treated as a baseline requirement, not an optional feature. All Piezee annotators sign strict NDAs before accessing any project data. Data is transferred via AES-256 encrypted channels, access is role-based and batch-restricted, and we do not retain any data after project delivery without explicit agreement. GDPR-compliant and HIPAA-compatible workflows are available for sensitive data types.
Most projects can begin with a paid pilot within 48–72 hours of requirement sign-off. For production scale, we typically ramp annotation teams within one week of pilot approval. Piezee maintains a pre-trained, managed workforce that can scale from a small team to 100+ annotators on short notice — making us suitable for both early-stage AI startups and large enterprise ML operations.
Piezee works with industry-standard annotation platforms including Labelbox, CVAT, Label Studio, Roboflow, SuperAnnotate, V7, and Scale AI. We deliver datasets in COCO JSON, Pascal VOC XML, YOLO TXT, CSV, and other custom formats. We can also integrate directly with your existing annotation infrastructure or internal tooling — our team adapts to your stack.
Building an in-house annotation team requires recruitment, onboarding, benefits, management bandwidth, tool licenses, and ongoing training overhead. Piezee handles all of this for you. Most clients reduce their annotation cost by 60–80% compared to in-house operations, while improving turnaround times and maintaining higher accuracy — because annotation is our core business, not a side function.
Yes, and we recommend it. Every new project starts with a structured pilot phase — typically 200 to 500 annotated samples — so that both teams can calibrate the annotation guidelines, review quality together, and identify edge cases before production begins. Pilots are priced per asset and credited toward the first full production batch on most projects.
Tell us about your annotation project. We'll respond within one business day with a customized quote, timeline, and team recommendation — no generic forms, no automated chatbots.
✓ NDA available on request ✓ Free pilot for qualifying projects ✓ Response within 24 hours