Annotation Services Hub

Professional Data Annotation Across Every Modality

Domain-trained annotation teams with multi-tier QA, measurable accuracy benchmarks, and continuous model-feedback loops. From sub-pixel image segmentation to RLHF preference ranking — we deliver production-grade labeled data with the quality controls your ML pipeline demands.

Image Video Text/NLP Medical/DICOM Audio/Speech LLM/GenAI
Request a Pilot View Pricing
10M+
Labels Delivered
99.2%
Average Accuracy
6
Data Modalities
50+
Enterprise Clients
L1→L2→L3
QA Pipeline
κ ≥ 0.85
Inter-Annotator Agreement
Modalities

Six Annotation Modalities, One Quality Standard

Each modality has dedicated domain-trained teams, specialized QA protocols, and measurable accuracy benchmarks. Click any modality to explore its full capabilities.

Image Annotation

Sub-pixel precision across 8 annotation types

Bounding boxes, oriented bounding boxes, polygons, polylines, semantic segmentation, instance segmentation, panoptic segmentation, keypoint skeletons, and multi-label classification — all with sub-pixel precision and configurable IoU thresholds.

Bounding Box Oriented Box Polygon Semantic Seg. Instance Seg. Panoptic Seg. Keypoints Classification
Technical Specifications
  • IoU threshold: ≥ 0.92 (configurable to ≥ 0.95 for precision-critical projects)
  • Class taxonomy: unlimited hierarchical labels with parent-child relationships
  • Multi-annotator consensus: 3-way overlap with majority-vote adjudication
  • Sub-pixel vertex accuracy: < 2px deviation on polygon boundaries
  • Occlusion handling: partial visibility flags + truncation percentage annotation
  • Formats: COCO, Pascal VOC, YOLO, Cityscapes, LabelMe, custom JSON
Performance
throughput
10K–50K images/week
Quality Target
IoU ≥ 0.92
Key Industries
Autonomous Vehicles Manufacturing QC Retail Planograms Satellite/Aerial Agriculture

Video Annotation

Frame-accurate tracking with persistent object IDs

Multi-object tracking (MOT), temporal action segmentation, activity recognition, pose estimation across frames, event detection, and scene change annotation — with persistent IDs maintained through occlusions, re-entries, and camera cuts.

Object Tracking MOT with ReID Action Segmentation Pose Tracking Event Detection Scene Classification
Technical Specifications
  • Tracking accuracy: MOTA ≥ 0.90, ID switch rate ≤ 0.5% per sequence
  • Keyframe annotation + linear/spline interpolation with manual correction
  • Multi-camera support: cross-view identity linking with shared ID namespace
  • Persistent IDs: maintained through occlusions up to 60 frames
  • Temporal precision: ±1 frame accuracy on action boundaries
  • Formats: MOT Challenge, TAO, ActivityNet, custom JSON with frame-level metadata
Performance
throughput
500–2K video-minutes/week
Quality Target
MOTA ≥ 0.90
Key Industries
Surveillance Sports Analytics Autonomous Driving Robotics Retail Behavior

Text & NLP Annotation

30+ languages with domain-specific entity types

Named entity recognition (NER), relation extraction, coreference resolution, sentiment analysis, text classification, document structure parsing, key-value extraction from forms, and intent/slot labeling for conversational AI.

NER Relation Extraction Coreference Sentiment Classification Intent/Slot Key-Value Extraction
Technical Specifications
  • Entity-level F1: ≥ 0.93 with exact span matching
  • 30+ languages: native-speaker annotators with linguistic training
  • Domain taxonomies: ICD-10, SNOMED, NAICS, legal citation standards
  • Inter-annotator agreement: Cohen's κ ≥ 0.85 enforced
  • Custom entity schemas: unlimited types with nested/overlapping span support
  • Formats: CoNLL, IOB2, spaCy JSON, Prodigy JSONL, BRAT standoff, custom
Performance
throughput
50K–200K documents/month
Quality Target
κ ≥ 0.85
Key Industries
Healthcare NLP Legal Tech Financial Services Customer Support Multilingual AI

Medical / DICOM Annotation

Board-certified professionals with HIPAA compliance

Radiology (CT, MRI, X-ray), histopathology (whole-slide images), dermatology, ophthalmology (fundus, OCT), and dental imaging — annotated by board-certified medical professionals with full HIPAA/GDPR compliance and IRB-ready documentation.

ROI Segmentation Landmark Detection Lesion Classification Measurement Report Structuring Finding Grading
Technical Specifications
  • Annotator credentials: board-certified radiologists, pathologists, dermatologists
  • DICOM compliance: full header preservation, SOP Class validation
  • Multi-reader study design: 3–5 readers with consensus adjudication
  • Dice coefficient: ≥ 0.90 for segmentation tasks (organ, lesion, structure)
  • De-identification: 50+ PHI fields scrubbed, pixel-level face redaction
  • Formats: DICOM-SEG, DICOM-SR, NIfTI, NRRD, custom JSON with DICOM UIDs
Performance
throughput
2K–10K studies/month
Quality Target
Dice ≥ 0.90
Key Industries
Radiology AI Digital Pathology Dermatology Ophthalmology Clinical Trials

Audio & Speech Annotation

Timestamped labeling across 20+ languages and dialects

Verbatim transcription, speaker diarization, emotion/sentiment detection, intent classification, phonetic labeling (IPA), sound event detection, and accent/dialect tagging — with millisecond-level timestamp precision across 20+ languages.

Transcription Diarization Emotion Detection Intent Classification Phonetic Labeling Sound Event Detection
Technical Specifications
  • Word Error Rate (WER): ≤ 3% for clean audio, ≤ 8% for noisy environments
  • Timestamp precision: ±50ms for word boundaries, ±100ms for utterances
  • Speaker attribute labeling: age range, gender, accent, emotional state
  • Diarization Error Rate (DER): ≤ 5% with speaker overlap handling
  • 20+ languages with native-speaker transcribers and dialect awareness
  • Formats: CTM, TextGrid, ELAN, WebVTT, SRT, custom JSON with timestamps
Performance
throughput
1K–5K audio-hours/month
Quality Target
WER ≤ 3%
Key Industries
Voice Assistants Call Center AI Podcast/Media Robotics Accessibility

LLM & Generative AI Data

RLHF, instruction tuning, and safety evaluation by domain experts

Instruction-response quality rating, RLHF preference ranking (Bradley-Terry, Elo, best-of-N), safety red-teaming, factuality verification, prompt engineering evaluation, and constitutional AI alignment data — produced by domain-expert raters with structured rubrics.

Instruction Tuning RLHF Preference Safety Red-Team Factuality Check Prompt Eval Constitutional AI
Technical Specifications
  • Preference agreement: inter-rater κ ≥ 0.65 with 3+ independent raters
  • Red-team protocols: 200+ adversarial prompt categories with severity scoring
  • Factuality verification: claims checked against authoritative source databases
  • Rubric-calibrated scoring: 1–7 Likert scales across helpfulness, accuracy, safety
  • Domain coverage: medical, legal, financial, scientific, general knowledge
  • Formats: JSONL preference pairs, DPO-ready, reward model training format
Performance
throughput
5K–20K evaluations/week
Quality Target
κ ≥ 0.65
Key Industries
LLM Development Chatbot Safety Enterprise AI Search/Retrieval Content Moderation
Why UTL

What Sets Us Apart

We've solved the specific problems that ML teams face with traditional annotation vendors — inconsistent quality, rotating workforces, black-box processes, and platform lock-in.

Domain-Trained Annotation Teams

Our annotators aren't generic crowdworkers cycling through random tasks. Each team is recruited, trained, and calibrated for your specific domain. Healthcare teams know anatomy and DICOM conventions. Automotive teams understand traffic scenarios, occlusion patterns, and ODD coverage. Retail teams recognize planogram layouts and SKU hierarchies.

  • Domain-specific onboarding: 20–40 hour training program per project
  • Ongoing calibration: weekly gold-set recalibration with drift detection
  • Qualification exam: ≥ 90% accuracy on gold set before production access
  • Annotator specialization: average tenure 18+ months on domain-specific projects

Multi-Tier QA Pipeline (L1 → L2 → L3)

Every label passes through a three-layer quality system. L1 annotators produce initial labels. L2 reviewers audit 100% of output against gold standards. L3 adjudicators resolve disagreements and handle edge cases. Gold set calibration, inter-annotator agreement tracking, and per-class accuracy dashboards ensure consistency at scale.

  • L1 Annotation: domain-trained annotators with task-specific guidelines
  • L3 Adjudication: expert panel resolves disagreements and edge-case ambiguity
  • IAA monitoring: Cohen's κ computed per batch with < 0.80 triggering recalibration
  • L2 Review: senior reviewers audit 100% of L1 output (not sampling — full coverage)
  • Gold set refresh: 10% of gold set replaced monthly to prevent memorization

Enterprise-Grade Security

SOC 2 Type II aligned processes. Data never leaves controlled environments. We provide the security infrastructure that enterprise ML teams require — not as premium add-ons, but as standard operating procedure for every engagement.

  • Data isolation: project-level tenant separation with no cross-project data access
  • RBAC: role-based access control with audit logging on every annotation action
  • Workforce controls: background checks, device management, secure facilities
  • Encryption: AES-256 at rest, TLS 1.3 in transit
  • NDA/DPA: executed before any data transfer, covering all team members

Transparent Real-Time Reporting

No black boxes. Weekly dashboards show exactly what's happening — throughput velocity, accuracy by class, annotator-level performance, IAA scores, error category breakdown, and quality trend lines. You see the same metrics we use to manage the team.

  • Real-time dashboard: throughput, accuracy, IAA, error distribution updated hourly
  • Annotator leaderboard: individual performance with percentile ranking
  • Weekly sync reports: executive summary + detailed appendix with raw metrics
  • Per-class accuracy: confusion matrices showing precision/recall per label
  • Trend analysis: accuracy and throughput trends over time with drift alerts

Continuous Model-Feedback Loop

We don't just label data in isolation — we integrate with your training pipeline. Model predictions feed back into annotation priorities through active learning. We label the samples that matter most for your next model iteration, not just what's next in the queue. This closes the loop between annotation and model improvement.

  • Active learning integration: uncertainty/diversity sampling from your model
  • Edge-case harvesting: model low-confidence samples prioritized for labeling
  • Guideline evolution: living documents updated based on model feedback signals
  • Error analysis: model failure patterns inform annotation guideline updates
  • Retraining validation: before/after accuracy deltas tracked per annotation batch

Flexible Engagement Models

Start with a 10-day pilot pod to validate quality and workflow fit. Scale to dedicated monthly teams for sustained production. Burst to 5× capacity for high-volume sprints. Zero lock-in — you own your data, your guidelines, and your quality benchmarks.

  • Pilot Pod: 10-day trial, 5–8 annotators, full QA pipeline, no commitment
  • Burst Capacity: 2–5× scale within 48 hours using pre-qualified bench
  • Transition support: full documentation handover if you ever move in-house
  • Dedicated Pod: monthly retainer, named team, PM + QA lead + annotators
  • Zero lock-in: all guidelines, gold sets, and labeled data are yours
Engagement Process

From First Call to Production Labels in 22 Days

A structured, transparent engagement process with clear deliverables at every stage. No surprises, no hidden steps — just a proven path from requirements to production-quality labeled data.

1

Discovery & Requirements Scoping

Day 1–3

We conduct a structured requirements workshop to understand your ML pipeline, model architecture, current pain points, and quality expectations. Output: a detailed Annotation Specification Document covering taxonomy, edge-case definitions, quality thresholds, and acceptance criteria.

Deliverables

  • Annotation Specification Document
  • Edge-case decision tree
  • Taxonomy definition with examples
  • Quality threshold agreement (IoU, κ, F1 targets)
2

Guideline Co-Creation & Gold Set Design

Day 3–7

We co-create detailed annotation guidelines with your team — including visual examples, counter-examples, and decision flowcharts for ambiguous cases. Simultaneously, we design the gold set (100–500 expert-labeled samples) used for annotator calibration and ongoing quality monitoring.

Deliverables

  • Illustrated annotation guidelines (v1.0)
  • Decision flowcharts for edge cases
  • Gold set: 100–500 expert-labeled samples
  • Quality rubric with scoring criteria
3

Team Assembly & Calibration

Day 7–12

We recruit and assemble your annotation pod — annotators, reviewers, QA lead, and project manager — all selected for domain expertise. Every team member completes your domain training program and passes a gold-set qualification exam (≥ 90% accuracy) before touching production data.

Deliverables

  • Named team roster with domain backgrounds
  • Calibration session recordings
  • Qualification exam results (≥ 90% pass rate)
  • Annotator agreement baseline (κ measurement)
4

Pilot Production (10-Day Trial)

Day 12–22

A controlled pilot on 1,000–5,000 representative samples. Full QA pipeline active from day one. Daily quality reports, guideline refinements based on edge cases discovered, and accuracy convergence tracking. You validate output quality before committing to scale.

Deliverables

  • 1K–5K labeled samples with full QA
  • Guideline v2.0 (refined from pilot discoveries)
  • Daily accuracy reports + trend charts
  • Gold set v2.0 (expanded with pilot edge cases)
5

Scale Production & Continuous QA

Ongoing

Full-scale annotation with real-time quality dashboards, weekly sync meetings, and continuous guideline evolution. Active learning integration routes high-value samples to human review. Monthly optimization reports identify accuracy improvements and cost efficiencies.

Deliverables

  • Real-time quality dashboard access
  • Monthly optimization report
  • Weekly sync meetings with PM + QA lead
  • Guideline version control with changelog
6

Delivery, Iteration & Model Feedback

Per batch

Labeled data delivered in your preferred format (COCO, VOC, YOLO, JSONL, custom) with full provenance metadata. Model feedback loops into annotation priorities — we label what matters most for your next training iteration. Continuous improvement, not one-shot delivery.

Deliverables

  • Formatted labeled data + provenance metadata
  • Model feedback integration (active learning queue)
  • Delivery acceptance report (volume, accuracy, distribution)
  • Transition documentation (if moving in-house)

Engagement Timeline at a Glance

Step 1

Day 1–3

Step 2

Day 3–7

Step 3

Day 7–12

Step 4

Day 12–22

Step 5

Ongoing

Step 6

Per batch

First labeled batch delivered by Day 22. Full-scale production from Day 23 onward.

COMPARISON

UTL Annotation vs. Typical Vendors

Capability UTL Data Engine Typical Providers
Domain-trained annotators (20+ hr onboarding) 2–4 hr generic training
3-tier QA (L1 → L2 → L3) with 100% review Sampling-based QA
Gold set calibration with monthly refresh
Inter-annotator agreement tracking (κ) Not measured
Per-class accuracy dashboards Aggregate only
Active learning / model feedback integration
Board-certified medical annotators General crowd
Named, dedicated team (not rotating crowd)
Zero lock-in (you own everything) Platform lock-in
10-day pilot pod (no commitment) Annual contracts
FAQS

Annotation Services Questions

Most vendors sample 5–10% of annotations for review. We review 100% — every single label passes through L2 review. L3 adjudication handles disagreements and edge cases. Gold set calibration (refreshed monthly) ensures annotators don't drift. The result: 99.2% average accuracy vs. the industry's 85–90% with sampling-based QA.
Yes. Many ML projects require image + text, video + audio, or DICOM + clinical text annotation. We assemble cross-modality teams with shared guidelines and unified quality dashboards. A single PM coordinates across modalities so you have one point of contact, not six.
We annotate 1,000–5,000 representative samples with the full QA pipeline active from day one. You receive daily accuracy reports, guideline refinements, and trend charts. By day 10, you have validated output quality, refined guidelines (v2.0), and an expanded gold set — all before committing to scale.
During guideline co-creation, we build decision flowcharts for known ambiguities. During production, new edge cases are flagged, escalated to L3 adjudication, and resolved with documented decisions that update the guideline. Every edge-case resolution becomes a new gold-set example, preventing the same ambiguity from recurring.
Our bench workforce can scale your team 2–3× within 48 hours. All bench annotators are pre-qualified on your domain and have completed baseline training. For new domains, allow 1–2 weeks for full onboarding and calibration. We've scaled from 5 to 50 annotators within one week for burst projects.
Absolutely. All annotation guidelines, gold sets, decision logs, and labeled data are yours — transferred with full documentation on engagement completion. Zero platform lock-in. If you ever move annotation in-house, we provide complete transition documentation including annotator training materials.
“We switched from a major crowd-annotation platform to UTL after discovering 15% label errors in our production dataset. UTL's L1→L2→L3 pipeline brought our error rate below 1% within the pilot period. The domain-trained team understood our medical imaging taxonomy from day one — no ramp-up surprises.”
ML Engineering Lead
Series B Medical AI Company

Ready to Start?

Describe your annotation needs — modality, volume, domain, and quality targets — and we'll scope a pilot within 48 hours. No commitment required.

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