Data annotation, RLHF, multimodal and multilingual AI data, and how Globik AI works.
Company mission, the workforce model, how Globik compares, and who we serve.
Globik AI is an AI data operations company that provides data annotation, data validation, and AI training data services. Globik combines a verified network of subject-matter experts (SMEs) with a purpose-built annotation platform, Globik Workforce, to help AI labs and enterprises turn raw, unstructured data into clean, model-ready training data.
Globik Workforce is a marketplace-plus-platform model for AI data work. It combines three things that are usually offered separately in this market: a verified, AI-interviewed pool of subject-matter experts; a project-matching marketplace that connects those experts to client projects; and a built-in annotation execution platform to actually complete the labeling, validation, and review work, all in one connected loop.
Most providers in this market fall into one of three buckets: crowd-sourced platforms with little verification (for example, large gig-worker pools), closed full-time-employee annotation shops with no marketplace, or SaaS tools that provide software but no talent.
Globik AI is built to combine all three strengths: a verified expert marketplace, multimodal annotation capability, and an in-house platform, all from an India-based cost structure that most competitors do not offer together.
Globik AI serves AI labs, enterprise technology companies, and startups that need human-verified training, validation, or evaluation data for machine learning and AI models, including companies building large language models (LLMs), computer vision systems, speech and conversational AI, autonomous vehicles, and healthcare AI.
Yes. Globik AI operates from India and builds its verified subject-matter-expert network primarily from the Indian talent pool, which allows it to offer expert-level annotation and evaluation work at a significantly lower cost than US-based expert marketplaces, while still serving international enterprise clients.
What annotation and labeling actually mean, the data types involved, and why they decide model quality.
Data annotation is the process of labeling raw data such as images, video, text, or audio with tags, categories, or metadata so a machine learning model can learn from it. Annotated data teaches an AI model what it is looking at, hearing, or reading (for example, marking the boundaries of a car in an image, or tagging the sentiment of a sentence).
Data labeling is the practical, hands-on task of assigning labels to individual pieces of data: a bounding box on an object, a transcript for an audio clip, a category for a document. It is often used interchangeably with ‘data annotation’, though annotation is sometimes used as the broader term covering the whole workflow, and labeling as the specific task of applying tags.
In practice the two terms overlap heavily. ‘Data labeling’ typically refers to the specific act of tagging a data point, while ‘data annotation’ can also include the surrounding workflow: task design, quality review, edge-case handling, and validation. Globik’s platform covers the full annotation workflow, not just raw labeling.
AI models learn by example. A model is only as accurate as the data it is trained on. Poorly labeled, inconsistent, or unverified data leads to biased or unreliable models.
High-quality, expert-verified annotation directly improves model accuracy, reduces hallucinations and errors, and is often the single biggest lever for improving AI performance after the model architecture itself.
Common data types include images, video, text and documents, audio and speech, and 3D data such as LiDAR point clouds. Globik AI supports multimodal annotation, meaning a single client can get text, vision, speech, and specialized data types like medical or geospatial data annotated through one platform.
Multimodal data annotation is the ability to label and validate more than one type of data, for example text, images, audio, and video, within a single connected workflow, often for AI models that must understand multiple input types at once (such as models that process both an image and its caption). Globik AI is built to handle multimodal projects rather than a single narrow data type.
Data annotation is used across nearly every industry building AI: technology and software (LLMs, search, recommendation systems), automotive (autonomous vehicles), healthcare (medical imaging and diagnostics AI), finance (fraud detection, document processing), retail and e-commerce (computer vision, personalization), and government and public sector (geospatial and satellite analysis).
Bounding boxes, polygons, segmentation, and frame-by-frame video labeling for computer vision.
Image annotation is the process of labeling objects, regions, or attributes within an image so a computer vision model can recognize them. Common techniques include bounding boxes, polygons, keypoints, and pixel-level segmentation.
Globik AI’s expert annotators handle image labeling tasks such as bounding boxes, polygon annotation, classification, keypoint annotation, and image segmentation, across use cases ranging from general computer vision to specialized domains like medical imaging and autonomous vehicle perception.
Image segmentation is a precise form of image annotation where every pixel in an image is assigned to a category, for example separating ‘road’, ‘pedestrian’, and ‘vehicle’ pixel by pixel. It is more granular than a bounding box and is typically used where exact object shape matters, such as autonomous driving or medical imaging.
Video annotation is the process of labeling objects, actions, or events across a sequence of video frames, often tracking the same object as it moves through the scene. It is used to train models for object tracking, activity recognition, and autonomous systems.
Video annotation is widely used for autonomous vehicle perception systems, security and surveillance analytics, sports analytics, retail behavior analysis, and content moderation of video platforms.
Entity, intent, and sentiment labeling for language models, chatbots, and voice assistants.
Text annotation is the process of labeling written content, whether sentences, documents, or chat transcripts, with categories such as sentiment, intent, entities, or topic, so an NLP or language model can learn to interpret language accurately.
NLP (Natural Language Processing) data annotation covers tasks like Named Entity Recognition (NER), part-of-speech tagging, sentiment analysis, intent classification, and text summarization, labeling the building blocks that let a model understand and generate human language.
NER annotation is the task of tagging specific entities in text, such as people, organizations, locations, dates, or product names, so a model learns to identify and classify them automatically in new text.
Conversational AI data annotation involves labeling and evaluating chatbot or voice-assistant conversations, rating response quality, tagging intents, flagging errors, and ranking alternative responses, to improve how a conversational AI model understands and responds to users.
Transcription, speaker labeling, and sound-event tagging, including regional Indian languages.
Audio annotation is the process of labeling sound data, for example transcribing speech, tagging speaker identity, marking background noise, or classifying sound events, so an AI model can learn to process audio accurately.
Speech data annotation is used to train speech-to-text systems, voice assistants, call-center analytics tools, and speaker identification systems. It typically includes transcription, timestamping, speaker labeling, and accent or dialect tagging.
Yes. Globik AI has delivered multilingual and regional-language annotation work, including speech transcription in Indian languages such as Telugu, Tamil, and Bengali, a capability gap for many global annotation providers, who are typically strongest in English and a small set of major world languages.
Human feedback, preference ranking, evaluation, and fine-tuning data for large language models.
RLHF is a technique used to fine-tune large language models by having human reviewers rank or rate different model outputs. Those human preferences are used to train a reward model, which then guides the AI to produce more helpful, accurate, and safe responses.
RLHF is a core reason modern chatbots follow instructions well and avoid harmful outputs.
Without RLHF, a raw language model is good at predicting the next word but is not reliably helpful, safe, or aligned with what a user actually wants. RLHF is the human-feedback layer that turns a raw model into a usable, instruction-following assistant, and it depends entirely on skilled human reviewers producing consistent, high-quality feedback.
LLM training data is the labeled, ranked, or validated data used at every stage of building a large language model, from pretraining data curation, to instruction-tuning examples, to RLHF preference rankings, to red-teaming and safety evaluation data.
Yes. Globik AI’s verified subject-matter experts support RLHF workflows, including response ranking, preference labeling, instruction-following evaluation, and safety and red-teaming style review, for teams building or fine-tuning language models.
Model evaluation and benchmarking is the process of systematically testing an AI model’s outputs against defined criteria such as accuracy, safety, helpfulness, bias, and task completion, usually with human expert reviewers scoring or comparing outputs. It is used both during development and to track a model’s performance over time.
Fine-tuning data is a curated, often smaller, set of high-quality labeled examples used to adapt a general-purpose language model to a specific task, domain, or behavior (for example, teaching a model to follow a particular tone, format, or subject-matter expertise) after its initial pretraining.
Native-speaker data collection and annotation across Indian regional languages and beyond.
Yes. Globik AI collects, annotates, and validates AI training data across multiple languages, with particular strength in Indian regional languages that most global providers cover poorly.
Globik AI has delivered annotation and transcription work in Indian languages including Telugu, Tamil, and Bengali, alongside broader multilingual and English-language capability, and continues to expand language coverage based on client needs.
India is home to hundreds of millions of internet users across dozens of major languages, yet most large AI models remain far weaker in Indian languages than in English.
Companies building AI products for the Indian market, or building genuinely multilingual global models, need native-speaker-verified data in these languages, an area where India-based providers like Globik have a structural advantage over US or Europe-based competitors.
Clinically reviewed imaging and records annotation, and how privacy frameworks are handled.
Medical image annotation is the process of labeling clinical images such as X-rays, MRIs, CT scans, and pathology slides with structures, abnormalities, or diagnoses, typically requiring reviewers with clinical or medical training so the labels are clinically accurate.
Globik AI designs its data handling and annotation workflows with privacy-sensitive domains like healthcare in mind, aiming to align with the data protection principles behind frameworks such as GDPR and HIPAA.
Clients with specific regulatory or compliance requirements should confirm project-level requirements directly with the Globik team before onboarding sensitive health data.
Medical and healthcare annotation use cases include diagnostic imaging annotation (radiology, pathology), clinical text and records annotation, and expert review of medical AI model outputs, work that benefits from domain-trained reviewers rather than generalist crowd annotators.
LiDAR point clouds, sensor fusion, and satellite imagery labeling for perception and mapping.
3D point cloud annotation is the process of labeling objects within 3D spatial data, most commonly LiDAR sensor data, by drawing 3D bounding boxes or segmenting points to identify vehicles, pedestrians, obstacles, and road surfaces. It is a core data type for autonomous vehicle and robotics perception systems.
Autonomous vehicle data annotation covers the labeling of camera, radar, and LiDAR sensor data, including 3D point clouds, video object tracking, and lane and road segmentation, that self-driving perception systems use to detect and understand their surroundings.
Yes. 3D point cloud and LiDAR-style annotation is part of Globik AI’s data annotation capability for autonomous vehicle, robotics, and geospatial use cases.
Geospatial annotation is the labeling of satellite or aerial imagery, for example marking land use, buildings, roads, or environmental change over time, used for mapping, agriculture, defense, and urban planning AI applications.
Prompt-response evaluation, synthetic dataset creation, and policy-based content review.
Generative AI data enablement refers to the full set of human-data services needed to build and improve generative AI models, including prompt-response evaluation, output ranking, instruction data creation, and safety review, beyond traditional labeling of static datasets.
Synthetic data generation is the creation of artificial, privacy-safe datasets that simulate real-world scenarios, used when real data is scarce, sensitive, or too rare to collect at scale (for example, rare accident scenarios for autonomous vehicles, or rare disease cases for medical AI).
Content moderation is the review and classification of user-generated content, whether text, images, or video, against safety and policy guidelines, to detect harmful, abusive, or policy-violating material. It requires trained human reviewers working with clear guidelines and escalation processes.
Globik AI’s platform and expert network are positioned to support synthetic data and content moderation workstreams as part of its broader AI data operations capability, alongside its core annotation and RLHF services. Details of scope are confirmed per client engagement.
How domain experts are sourced, screened, and matched to projects on the Globik Workforce.
An SME marketplace is a platform where domain experts, such as doctors, lawyers, engineers, linguists, or industry specialists, can register, get vetted, and get matched to AI data projects that require their specific expertise, rather than relying on generalist crowd workers.
The Globik Workforce expert pool is built from India-based subject-matter experts across relevant domains, verified through a screening and AI-assisted interview process before being matched to client projects, rather than open, unscreened sign-up.
Experts go through a screening and interview process designed to verify domain expertise and task-suitability before being matched to live projects, the same verified-before-deployed model used by leading global expert marketplaces, applied to Globik’s India-based talent pool.
How verified annotation differs from crowd work, and what quality control looks like in practice.
Globik AI works with a verified pool of subject-matter experts who go through a screening process before being matched to projects, rather than relying on an open, unscreened crowd, with the goal of higher consistency and domain accuracy than crowd-based platforms.
Crowd-sourced annotation platforms allow largely anyone to sign up with minimal screening, which can produce inconsistent quality at very large scale and low cost.
Verified annotation platforms screen and test contributors, often for specific domain expertise, before allowing them onto live projects, prioritizing accuracy and consistency over unlimited scale.
As AI models are used in higher-stakes settings (healthcare, finance, safety-critical systems), the cost of bad training data grows. Enterprises are increasingly liable for AI outputs built on their training data, which makes auditable, verified, domain-expert annotation a bigger priority than simply maximizing the number of available workers.
Globik AI’s workflow is built around expert-level review within its Workforce platform. Verified SMEs handle annotation and validation, with quality control built into the platform’s review process rather than treated as a separate afterthought.
Responsible sourcing, privacy practice, and how regulated data should be scoped with the team.
Responsible AI data sourcing means building training datasets using fairly compensated, appropriately vetted human contributors, with transparent workflows and traceable data handling, as opposed to opaque, low-wage crowd-labor pipelines that have drawn increasing scrutiny in the AI industry.
Globik AI’s model is built around a verified, India-based expert network rather than an anonymous global crowd, positioning it as a more transparent and auditable alternative to providers whose annotation workforces have faced ethical scrutiny over low wages and lack of verification.
Client data submitted for annotation is handled through Globik’s platform workflows with privacy-conscious practices appropriate to the sensitivity of the data involved.
Clients with specific security, compliance, or data-residency requirements should discuss those requirements directly with the Globik team as part of project scoping.
Globik AI aims to design its data handling processes in line with GDPR-relevant privacy principles, particularly for European clients or data subjects. As with any vendor, clients with strict regulatory obligations should confirm specific compliance requirements before sending regulated data.
What annotation costs, who Globik works with, and how to scope a first project.
Annotation pricing varies widely by task complexity, data type, required expertise level, and volume, from simple crowd-sourced microtasks costing cents per item, to premium expert marketplaces charging $150 to $200 per hour for highly specialized reviewers.
Globik AI’s India-based verified expert model is designed to offer expert-level quality at meaningfully lower cost than US-based premium marketplaces.
Yes. Globik AI’s positioning specifically targets the mid-market and growth-stage AI companies (roughly Series A to C) that are often priced out of $50,000+ minimum engagements at premium US-based marketplaces, in addition to serving larger enterprise clients.
Project scope and minimums depend on the specific engagement: data type, volume, and complexity. Prospective clients should contact the Globik team directly to scope a project and get a quote suited to their size and requirements.
You can get started by reaching out to the Globik AI team through the Globik Workforce at workforce.globik.ai to discuss your data annotation, validation, or AI training data needs and scope a project.
Yes. Globik AI’s platform model supports both project-based engagements and ongoing annotation, validation, and evaluation programs, similar to how enterprise clients maintain continuous data pipelines with other annotation vendors.
Tell us your data type, volume, and the expertise it needs, and we will scope the right expert team for it.
