Annotexa

Key Skills

Software

CVAT

Roboflow

Supervisely

Other

Appen

Labelbox

Top Subject Matter

Information technologies

Top Data Types

Image

Text

Document

Top Task Types

Segmentation

Bounding Box

RLHF

Evaluation/Rating

Question Answering

Company Overview

Annotexa AI is an AI data operations and annotation company specializing in scalable Human-in-the-Loop (HITL) workflows, AI training data preparation, and quality-driven annotation services for enterprise AI systems. Our mission is to bridge the gap between advanced AI engineering and high-quality human intelligence operations by delivering reliable, scalable, and secure data solutions for machine learning and generative AI applications. We support a wide range of AI data services including LLM evaluation, RLHF workflows, text annotation, image and video annotation, segmentation, transcription, data labeling, QA review, and multilingual annotation operations. Our team has experience working on projects related to Computer Vision, NLP, Generative AI, and enterprise AI automation pipelines. Annotexa AI operates with a scalable workforce model capable of rapidly deploying trained annotators, reviewers, and QA specialists across multiple domains and project requirements. We utilize industry-standard tools and platforms including CVAT, Label Studio, Scale Studio, Amazon SageMaker, and custom annotation workflows depending on client requirements. Our operational focus is centered around accuracy, security, scalability, and rapid turnaround time. We follow structured SOP-based workflows, multi-level quality review systems, and continuous training models to maintain consistent delivery quality across projects. We have supported projects involving LLM response evaluation, image annotation, document processing, PII tagging, AI workflow validation, and data operations support for AI-driven platforms and enterprise solutions.

Security

Labeling Experience

Image Classification & Quality Evaluation

OtherImageClassificationQuestion Answering

orked on image classification and quality evaluation projects involving object presence detection, image quality rating, and feedback validation workflows. Responsibilities included identifying target objects, evaluating image clarity, handling edge cases, and following SOP-based annotation standards. The project required accurate classification under varying image conditions including partial visibility, blur, lighting issues, and ambiguous object presence. Multi-level QA review processes and SOP compliance checks were implemented to ensure consistent annotation quality.

2026 - Present

LLM Response Evaluation & RLHF

OtherTextEvaluation/Rating

Worked on large-scale LLM response evaluation and reinforcement learning from human feedback (RLHF) projects focused on improving generative AI model performance. Responsibilities included evaluating AI-generated responses for instruction following, relevance, factuality, safety, coherence, ambiguity handling, and overall response quality. The project involved human preference ranking, quality scoring, justification writing, annotation validation, and multi-level QA review workflows. Annotators followed strict SOPs and calibration guidelines to maintain high-quality standards across diverse prompts and conversational datasets. The team also supported HITL workflows by providing structured human feedback to optimize model alignment and improve enterprise AI outputs. Quality assurance processes included audit reviews, accuracy tracking, SOP compliance checks, and continuous calibration sessions.

2026 - Present

Image Annotation & Computer Vision

OtherImageClassificationBounding Box

Worked on image annotation and computer vision projects involving object detection, bounding boxes, polygon annotation, semantic segmentation, image classification, and QA validation workflows for AI model training. The project included annotating diverse datasets under varying lighting conditions, camera angles, object densities, and annotation complexities. Tasks were performed following strict SOPs and quality calibration guidelines to ensure high annotation accuracy and consistency. The team supported large-scale AI data operations involving pre-annotation correction, manual labeling, quality review, audit handling, and dataset validation for machine learning and computer vision applications. Experience also included handling annotation workflows for retail objects, agricultural datasets, animals, products, and general object recognition tasks. Quality assurance processes involved multi-level QA reviews, calibration-based corrections, accuracy tracking, and continuous reviewer feedback to maintain enterprise-level data quality standards.

2025 - Present

Image Annotation & Security Screening

RoboflowImageBounding BoxClassification

Worked on X-ray image annotation projects focused on detecting prohibited and suspicious objects including guns, laptops, smoke bombs, knives, mobile phones, and other items in baggage screening datasets. Responsibilities included bounding box annotation, polygon segmentation, object classification, and quality validation for AI-based security screening systems. The project involved handling complex X-ray imagery with overlapping objects, low-visibility items, and varying object orientations. Strict annotation guidelines and QA review processes were followed to maintain high labeling accuracy and consistency across datasets.

2026 - 2026