Dserve AI Service

Data Annotation and QA

Data Annotation Services by Dserve AI provide high-precision labeling for images, text, audio, and video to train machine learning models. As industry experts, we utilize human-in-the-loop workflows and strict quality assurance to deliver flawless datasets that accelerate AI deployment.

See How It Works

Annotation Pipeline

Raw Asset Batch

Asset typeImages + Text

Volume198,800 files

TaxonomyCustom v2.3

IAA target99%+

Labeling Active

QA Report

99%+

Inter-Annotator Agreement score on all projects

3-Stage

QA review on every single annotation

10+

Annotation formats supported out of the box

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Agnostic Tooling Ecosystem

We don't force you into proprietary black-box software. Dserve AI integrates directly into your existing ML infrastructure, whether you use industry-standard platforms or custom internal labeling tools.

Industry-Standard Platforms

Seamless integration with leading annotation platforms, allowing our workforce to annotate directly within your active projects and ensuring real-time sync with your cloud storage.

Custom Internal Tools

Have a proprietary labeling UI? We deploy our trained workforce via secure VPNs to operate exclusively within your firewall-protected environments.

Dserve Annotation Engine

For teams without existing tools, we provide our proprietary annotation workspace, equipped with auto-segmentation and edge-snapping ML assists.

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The Labeling Matrix Engine

Interact with the nodes below to see how raw unstructured data transforms into a perfectly verified, 99%+ IAA dataset.

Valid: 99%

Edge Case

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Taxonomy & Tooling Setup

We build a precise labeling guide and configure the annotation workspace to match your model's exact output schema.

Before any annotator touches your data, we work with your engineering team to build a detailed annotation taxonomy. Every edge case is documented. Tools are configured to enforce your schema: bounding box aspect ratios, class hierarchies, and overlap rules. Every label is consistent from day one.

Taxonomy designSchema configurationEdge case docsTool setup

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QA Specialist Review

A second expert layer audits every annotation for accuracy, consistency, and schema compliance.

Our QA specialists work independently from the primary annotators. They audit a statistically significant sample per class, apply automated consistency checks, and manually review any flagged items. We maintain Inter-Annotator Agreement (IAA) scores above 99% on every project delivery.

Independent QA layerIAA 99%+Automated consistencyPer-class audits

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Primary Annotation Pass

Trained, domain-matched annotators label every asset under real-time quality supervision.

We match annotators to your domain: CV specialists for images, linguists for NLP, and clinical experts for medical data. Every annotator passes a task-specific calibration test before touching your dataset. You can monitor progress in real time with per-annotator accuracy tracking.

Domain-matched teamsCalibration testReal-time monitoringAccuracy tracking

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Export & Integration

Delivered in your format, ready to plug directly into your training pipeline.

We handle format conversion for COCO JSON, YOLO TXT, Pascal VOC XML, CONLL, CSV, or custom schemas. We validate the output file structure before delivery and provide an integration guide so your engineering team can onboard the dataset seamlessly.

COCO JSONYOLOPascal VOCCONLLCustom formats

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Enterprise-Grade Data Security

Your raw data is your competitive advantage. We enforce strict physical and digital security protocols to ensure zero data leakage, IP theft, or compliance violations.

GDPR Compliant Infrastructure
Automated PII/PHI Blurring Before Human Review
Isolated VPCs with No Internet Access for Annotators
Clean-Room Facilities with Biometric Access Control

Zero-Retention Policy

Upon project completion and secure delivery of the annotated dataset, all raw and processed data is cryptographically wiped from our servers within 72 hours.

We provide cryptographic certificates of destruction to satisfy your internal compliance audits.

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Handling Ambiguity & Edge Cases

Models fail on edge cases. Our project managers build living taxonomy documents that evolve as annotators encounter ambiguous data, ensuring perfect consistency across the entire workforce.

Scenario	Amateur Approach	Dserve AI Protocol
Heavy Occlusion	Guess the bounding box size	Strict 15% visibility rule; tag as 'occluded_heavy' and use interpolation.
Undefined Class	Force-fit into nearest category	Halt labeling; escalate to PM for immediate taxonomy update and team re-training.
Blurry / Low Light	Skip or guess label	Route to specialized image-enhancement pipeline before secondary review.

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Accelerate your AI roadmap.

Deploy enterprise-grade data pipelines. Speak with our engineering team to architect a custom solution for your proprietary models.

Start a Pilot Project View All Services

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