Globik AI delivers domain-specific data annotation services and production-grade AI training data across 24+ verticals - staffed by verified subject matter experts in your exact field, never generalist crowds.
Clinical AI lives or dies by the quality of its medical AI training data.
Context
Global healthcare AI market revenue is projected to exceed $188 billion by 2030.
Threshold
Healthcare AI models need clinical-grade annotation.
Resolution
Most annotation vendors apply the same generalist workforce to every domain.
Clinical-grade medical image annotation and data labeling by verified medical professionals.
Clinical document annotation by verified medical professionals
Medical transcription and structured data extraction
Radiology report labeling and imaging data annotation
ICD-10 and CPT coding validation by domain-trained annotators
Legal language is precise. The people labeling it need to be too.
Context
Contract review and legal document AI is growing faster than almost any enterprise application category.
Threshold
Clause-level annotation requires understanding not just what a clause says, but what it means in legal context.
Resolution
Crowd platforms can annotate at speed.
Globik AI supports legal AI systems with structured Clause-level contract and compliance data annotation by legal professionals.
Contract clause labeling by qualified legal professionals
Legal document classification across jurisdiction types
Case law annotation and legal entity recognition
Compliance data structuring for regulatory AI applications
Financial AI requires accuracy where the cost of error is measured in money.
Context
Financial services AI spending is growing at over 16% annually.
Threshold
Financial documents contain regulatory language, numerical data, entity relationships, and risk signals that require financial domain knowledge to label correctly.
Resolution
Financial annotation requires understanding of regulatory structures, document formats, and risk indicators that generalist annotators are not equipped to handle.
Financial data annotation where the cost of error is measured in money.
Financial document annotation by finance-trained SMEs
Fraud signal and transaction behavior labeling
Regulatory compliance data structuring
Earnings call and investor communication transcription and annotation
What the model sees is only as useful as what the annotator understood.
Context
Computer vision powers over 60% of enterprise AI deployments globally.
Threshold
A medical image annotated without clinical context produces a clinical AI with systematic errors.
Resolution
Generic annotation platforms can label at high volume.
Domain-matched experts behind every bounding box, polygon, and frame.
Image classification and multi-label annotation by domain-matched SMEs
Object detection, bounding box, and polygon annotation
Video scene annotation, action recognition, and frame-level labeling
Medical imaging annotation including radiology, pathology, and dermatology
ASR accuracy in regional languages is only achievable with native speakers.
Context
ASR accuracy in non-English and regional languages remains one of the hardest unsolved problems in voice AI.
Threshold
Transcription by a non-native speaker, or by a native speaker of a different dialect, introduces systematic errors that compound across a training dataset.
Resolution
Crowd platforms struggle to source verified native speakers for low-resource or regional languages.
Native-speaker transcription across 40+ languages and dialects.
Audio transcription by verified native speakers across 40+ languages
Dialect-aware annotation with per-region guideline documentation
Speech-to-text QA and accuracy validation
Speaker diarization, intent classification, and sentiment labeling
LLM quality comes down to the quality of its human feedback data.
Context
RLHF data quality is one of the primary differentiators between foundation models that perform reliably in production and those that do not.
Threshold
Instruction tuning, preference ranking, and safety annotation all require annotators who understand the domain the model will serve.
Resolution
Most RLHF vendors use general populations for preference labeling.
RLHF preference data from annotators who actually know the domain.
RLHF preference dataset creation with domain-matched annotators
Instruction-response pair generation at scale
Safety annotation and harmful output classification
Red-teaming data generation for adversarial AI testing
Product intelligence begins with product data quality.
Context
Retail AI spans product discovery, inventory optimization, visual search, and personalized recommendation.
Threshold
Product catalog annotation requires understanding of product categories, attribute hierarchies, and customer language.
Resolution
Generic annotation at retail scale prioritises throughput over attribute precision.
Catalog-scale attribute precision for search, ranking, and recommendation.
Product catalog annotation and attribute extraction
Product image classification and visual search labeling
Customer review sentiment annotation
Product-to-image matching and visual taxonomy labeling
Educational AI needs curriculum-aligned training data, not general text labels.
Context
AI-powered learning platforms are scaling rapidly.
Threshold
Educational content annotation requires understanding of learning levels, subject structures, assessment frameworks, and curriculum standards.
Resolution
Generic annotation of educational content produces models that are technically accurate at text classification but pedagogically incorrect.
Curriculum-aligned annotation by qualified educators.
Educational content annotation by subject-matter educators
Question-answer pair creation for AI tutor and assessment models
Reading level and complexity classification by qualified annotators
Curriculum alignment tagging across national and international standards
Frame-level precision across a 90-minute match requires annotators who understand the game.
Context
Sports analytics, broadcast AI, and performance intelligence applications all depend on high-quality, event-level annotation across video, audio, and structured match data.
Threshold
Event classification in sports video requires knowledge of the sport.
Resolution
General annotation workforces cannot reliably distinguish between event types in sports video without domain knowledge.
Frame-level event annotation by people who watch the game.
Sports video annotation including event classification, action recognition, and player tracking
Frame-level bounding box and keypoint annotation by sport-knowledgeable annotators
Match data structuring and performance metric labeling
Commentary and audio annotation for broadcast AI applications
Construction and infrastructure AI is safety-critical. Its training data needs to be treated that way.
Context
AI applications in construction and infrastructure include site safety monitoring, defect detection, progress tracking, and predictive maintenance.
Threshold
Infrastructure AI annotation requires understanding of engineering drawings, construction site conditions, defect types, and inspection frameworks.
Resolution
Generic annotation of construction site imagery, inspection reports, and engineering drawings by non-specialist annotators produces mislabeled training data that teaches AI models to miss the defects and hazards they are deployed to find.
Safety-critical annotation by engineering-trained reviewers.
Engineering drawing and technical document annotation
Construction site image and video annotation for safety and progress AI
Defect detection labeling for inspection and maintenance AI applications
Inspection report structuring and safety data annotation
Physical AI systems learn from the real world. The data that teaches them needs to reflect it with precision, including through the egocentric AI training data that captures the human perspective directly.
Context
Robotics and physical AI is one of the fastest-growing segments in applied AI.
Threshold
Physical AI annotation is fundamentally different from standard computer vision annotation.
Resolution
Annotation errors in physical AI training data are not abstract accuracy drops.
3D, egocentric, and embodied AI annotation by spatial domain specialists.
3D point cloud annotation and LiDAR data labeling for robotic perception and scene understanding
Robotic manipulation and trajectory labeling for pick-and-place, grasping, and dexterous task AI
Egocentric video annotation for first-person perspective AI, wearable systems, and embodied learning
Hand-object interaction labeling for fine-grained manipulation and egocentric action recognition
A self-driving system is only as safe as the autonomous vehicle training data it was trained on.
Context
Autonomous vehicles and mobility AI account for the largest single share of the global AI annotation market.
Threshold
Multi-sensor annotation for autonomous driving requires simultaneous understanding of 3D spatial geometry, object permanence across frames, depth and distance estimation, and scenario-specific edge case recognition.
Resolution
Generic annotation vendors apply 2D image labeling logic to inherently 3D spatial problems.
Precision LiDAR annotation and multi-sensor fusion labeling by domain-trained specialists.
LiDAR point cloud annotation and 3D bounding box labeling for vehicle and pedestrian detection
Camera and radar sensor fusion annotation for multi-modal perception systems
Lane detection, road segmentation, and HD map data labeling
ADAS scenario annotation across SAE Level 2 to Level 5 automation requirements
Location intelligence is only as precise as the geospatial AI training data that defines the world it sees.
Context
Geospatial AI is powering urban planning, climate monitoring, precision agriculture, disaster response, logistics optimization, and national infrastructure programs.
Threshold
Geospatial annotation is not standard image labeling.
Resolution
General annotation workforces are not trained in geographic information systems, remote sensing principles, or the visual signatures that distinguish land cover classes, infrastructure types, or change detection signals in satellite imagery.
Satellite imagery annotation and aerial data labeling by remote sensing and GIS-trained specialists.
Satellite and aerial imagery annotation for land use, land cover, and change detection
Building footprint extraction and urban infrastructure labeling
Drone imagery annotation for agricultural monitoring, site inspection, and environmental mapping
LiDAR-derived terrain and elevation data labeling for 3D geospatial models
Industrial AI learns from production data. The manufacturing AI training data behind it must reflect the factory floor, not a generic image dataset.
Context
Manufacturing and industrial AI is transforming quality control, predictive maintenance, assembly automation, and supply chain intelligence.
Threshold
Industrial annotation requires understanding of manufacturing processes, component types, defect morphologies, and tolerance thresholds that vary by material, production method, and industry standard.
Resolution
Generalist annotation applied to industrial inspection data produces models that miss defect types, misclassify fault signatures, and generate false positives that shut down production lines unnecessarily.
Industrial defect detection and machine vision annotation by engineering-trained domain specialists.
Visual quality inspection annotation for defect detection across materials and components
Machine vision training data for assembly line monitoring and process control AI
Predictive maintenance sensor data labeling including vibration, thermal, and acoustic signals
Industrial robot and automation training datasets for manipulation and navigation tasks
Public sector AI operates at national scale. The government AI training data behind it must meet the same standard.
Context
Governments across India, the Middle East, Europe, and the US are deploying AI for smart city infrastructure, public safety systems, national document digitisation, transport network optimization, and citizen service automation.
Threshold
Government AI annotation requires understanding of public sector document formats, administrative language, multilingual national contexts, regulatory classification standards, and data sovereignty requirements.
Resolution
Generic annotation vendors lack the multilingual depth, regulatory awareness, and domain knowledge of public sector operations required to annotate government AI training data accurately.
Government data annotation and public sector AI training data with audit-ready quality standards.
National infrastructure imagery annotation for transport, roads, and urban planning AI
Government document digitisation and administrative record classification
Multilingual public sector data annotation across national and regional languages
Smart city sensor and CCTV data labeling for traffic, safety, and urban monitoring AI
Tell us what you're building. We'll design the right approach the right SME mix, the right quality framework, and the right
AI data platform to deliver dependable, audit-ready AI training data across any domain or modality.
