# Best Data Labeling Software - Page 3 *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* Data labeling software helps data science and machine learning teams source, manage, annotate, and classify unstructured data, including text, images, videos, audio, and PDFs, into labeled datasets that create efficient training data pipelines for building and improving AI and ML models. ### Core Capabilities of Data Labeling Software To qualify for inclusion in the Data Labeling category, a product must: - Integrate a managed workforce and/or data labeling service - Ensure labels are accurate and consistent - Give the user the ability to view analytics that monitor the accuracy and speed of labeling - Allow annotated data to be integrated into data science and machine learning platforms to build machine learning models ### Common Use Cases for Data Labeling Software ML engineers, data scientists, and AI teams use data labeling tools to build high-quality training datasets across a wide range of application types. Common use cases include: - Annotating images, video, and text for computer vision, NLP, and speech recognition model training - Fine-tuning and evaluating large language models (LLMs) with human-labeled feedback data - Building training pipelines for object detection, named entity recognition, and sentiment analysis applications ### How Data Labeling Software Differs from Other Tools Data labeling is a foundational building block of the AI development lifecycle, distinct from the downstream tools it feeds. It integrates with [generative AI software](https://www.g2.com/categories/generative-ai), [MLOps platforms](https://www.g2.com/categories/mlops-platforms), [data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), [LLM software](https://www.g2.com/categories/large-language-models-llms), and [active learning tools](https://www.g2.com/categories/active-learning-tools) to support the full model development pipeline. ### Insights from G2 on Data Labeling Software Based on category trends on G2, labeling accuracy controls and workforce management features stand out as standout capabilities. Faster training data pipeline construction and improved model accuracy stand out as primary outcomes of adoption. ## How Many Data Labeling Software Products Does G2 Track? **Total Products under this Category:** 105 ### Category Stats (Jun 2026) - **Average Rating**: 4.52/5 The average rating of products in this category, based on all submitted ratings - **New Reviews This Quarter**: 37 - **Buyer Segments**: Small-Business 67% │ Mid-Market 27% │ Enterprise 6% Represents the distribution of reviewers across all products in this category. - **Top Trending Product**: FiftyOne (+0.153) - Among all products in this category, FiftyOne recorded the largest rating increase compared to last month *Last updated: June 01, 2026* ## How Does G2 Rank Data Labeling Software Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 1,700+ Authentic Reviews - 105+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Which Data Labeling Software Is Best for Your Use Case? - **Leader:** [SuperAnnotate](https://www.g2.com/products/superannotate/reviews) - **Highest Performer:** [BasicAI Data Annotation Platform](https://www.g2.com/products/basicai-data-annotation-platform/reviews) - **Easiest to Use:** [Roboflow](https://www.g2.com/products/roboflow/reviews) - **Top Trending:** [Encord](https://www.g2.com/products/encord/reviews) - **Best Free Software:** [SuperAnnotate](https://www.g2.com/products/superannotate/reviews) --- **Sponsored** ### Alteryx Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=2361&secure%5Bdisplayable_resource_id%5D=620&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=neighbor_category&secure%5Bplacement_resource_ids%5D%5B%5D=692&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=989&secure%5Bresource_id%5D=2361&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-labeling&secure%5Btoken%5D=b1b2ff6eee1df30a4ade6d773d83bcf30b9d7085ed5597d431153973090d7e5b&secure%5Burl%5D=https%3A%2F%2Fwww.alteryx.com%2Ftrial%3Futm_source%3Dg2%26utm_medium%3Dreviewsite%26utm_campaign%3DFY25_Global_AllRegions_AlwaysOn_AllPersonas_IndustryAgnostic%26utm_content%3Dg2_freetrial&secure%5Burl_type%5D=free_trial) --- ## What Are the Top-Rated Data Labeling Software Products in 2026? ### 1. [Bounding Boxes for Machine Learning and Computer Vision Datasets](https://www.g2.com/products/bounding-boxes-for-machine-learning-and-computer-vision-datasets/reviews) Data labeling services for bounding boxes in machine learning and computer vision datasets: draw a box around an area of interest and annotate it with a category from upto 10 categories. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind Bounding Boxes for Machine Learning and Computer Vision Datasets?** - **Seller:** [AnnotateIt](https://www.g2.com/sellers/annotateit) - **HQ Location:** Atlanta, GA - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market ### 2. [Labeling AI](https://www.g2.com/products/labeling-ai/reviews) Labeling AI is a deep learning-based technology that automatically labels large amounts of data based on a small amount of pre-labeled data available. Labeling AI is an innovative tool that can save your time. Auto labeling performs the labeling process of large datasets with minimal human intervention, required only to review the auto labeled data. Here is how it works in 3 simple steps: 1. Labeling Manually - Manually generate 100 labeled data. 2. Training Model - Train an auto labeling AI with the 100 pre-labeled data. Review and correct the results to enhance auto labeling performance. 3. Deploy the best AI - Repeat the previous step to generate 1,000, 10,000, or 100,000 auto-labeled data. Transform your auto labeling AI into an object detection AI model to perform object detection as needed. Labeling AI offers a variety of options to easily label your data, including bounding and polygon tools. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Labeling AI?** - **Labeler Quality:** 8.3/10 (Category avg: 8.9/10) - **Object Detection:** 10.0/10 (Category avg: 8.9/10) - **Data Types:** 8.3/10 (Category avg: 8.8/10) - **Ease of Use:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind Labeling AI?** - **Seller:** [DSLAB GLOBAL](https://www.g2.com/sellers/dslab-global-6ecf3847-7a64-4cb8-a386-2e140dd4103d) - **Year Founded:** 2007 - **HQ Location:** Miami, US - **LinkedIn® Page:** http://www.linkedin.com/company/dslaboratories (87 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 3. [LayerNext](https://www.g2.com/products/layernext/reviews) LayerNext is an AI-powered financial operations platform that helps businesses automate bookkeeping, bank reconciliation, accounts payable, and financial reporting. The platform uses specialized AI agents to process financial data, categorize transactions, reconcile accounts, manage invoice-based AP workflows, prepare reports, and support finance operations across accounting systems, ERPs, documents, bank feeds, and desktop applications. LayerNext helps small businesses, founders, finance teams, and enterprise organizations reduce manual finance work while keeping human review, approval workflows, validation gates, and audit trails in place. The platform supports workflows across QuickBooks, ERP systems, spreadsheets, documents, and legacy applications, making it useful for companies that need automation across both modern and older finance systems. What LayerNext does: - AI-powered bookkeeping - Invoice processing and AP automation - Bank reconciliation - Transaction categorization - Financial reporting - QuickBooks and ERP workflow automation - Cash flow, burn rate, and runway visibility - Approval workflows and audit trails - Automation across documents, bank feeds, spreadsheets, and desktop systems LayerNext gives finance teams a faster way to keep books accurate, manage financial workflows, and gain visibility into business performance without relying on repetitive manual processes. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate LayerNext?** - **Ease of Use:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind LayerNext?** - **Seller:** [LayerNext AI](https://www.g2.com/sellers/layernext-ai) - **Year Founded:** 2022 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/layernext/ (7 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise ### 4. [Surge AI](https://www.g2.com/products/surge-ai/reviews) We offer an Enterprise plan for teams that need high volume, fully managed data labeling services with guaranteed SLAs — we’ll help you create guidelines, build you custom labeling teams, and manage quality controls end to end. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Surge AI?** - **Labeler Quality:** 10.0/10 (Category avg: 8.9/10) - **Object Detection:** 10.0/10 (Category avg: 8.9/10) - **Data Types:** 10.0/10 (Category avg: 8.8/10) - **Ease of Use:** 10.0/10 (Category avg: 8.8/10) **Who Is the Company Behind Surge AI?** - **Seller:** [Surge AI](https://www.g2.com/sellers/surge-ai) - **Year Founded:** 2020 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/surge-ai/ (184 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Surge AI's Pros and Cons? **Pros:** - Customer Support (1 reviews) - Data Labelling (1 reviews) - Ease of Use (1 reviews) - Efficiency (1 reviews) - Helpful (1 reviews) **Cons:** - Expensive (1 reviews) - Lack of Features (1 reviews) - Limited Tools (1 reviews) ### 5. [TaQadam Image Annotation](https://www.g2.com/products/taqadam-image-annotation/reviews) TaQadam means Progress. TaQadam is a female founded startup that aims to advance economic opportunity for youth and democratize GEO-AI. TaQadam develops imagery solutions for market intelligence, monitoring and measuring of business risks and vulnerabilities. We believe the development of a global map of physical assets and infrastructure is essential in today’s context. Identifying assets (e.g. mines, farm equipment, schools) and their features (e.g. cooling, water tanks, construction materials) is a way to build alternative data for sustainable growth and risk mitigation. Extending an economic opportunity to disadvantaged youth through our image annotation work is at the core of our business. **Average Rating:** 3.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate TaQadam Image Annotation?** - **Object Detection:** 8.3/10 (Category avg: 8.9/10) - **Data Types:** 8.3/10 (Category avg: 8.8/10) - **Ease of Use:** 8.3/10 (Category avg: 8.8/10) **Who Is the Company Behind TaQadam Image Annotation?** - **Seller:** [TaQadam](https://www.g2.com/sellers/taqadam) - **Year Founded:** 2017 - **HQ Location:** New York, US - **LinkedIn® Page:** https://www.linkedin.com/company/18244610 (7 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 6. [TrainingSet.AI Image And LiDAR Annotation Platform](https://www.g2.com/products/trainingset-ai-image-and-lidar-annotation-platform/reviews) Trainingset.ai Platform receive your instructions and data via API call, Dashboard form or CSV upload, then your annotators in conjunction with our annotation & smart tools, AI and a Quality Assurance process, will help your annotators to resolve the task accurately in a very short time frame by, for example, annotating, labeling or categorizing your image or point cloud data **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind TrainingSet.AI Image And LiDAR Annotation Platform?** - **Seller:** [trainingset.ai](https://www.g2.com/sellers/trainingset-ai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market ### 7. [Universal Data Tool](https://www.g2.com/products/universal-data-tool/reviews) The Universal Data Tool is a web/desktop app for editing and annotating images, text, audio, documents and to view and edit any data defined in the extensible .udt.json and .udt.csv standard. Collaborate with others in real time, easily train labelers, integrate into your applications. Perform Image Segmentation, Image Classification, Audio Transcription, Named Entity Recognition (NER) and Named Entity Linking (NEL). Run with docker, use with Tensorflow, Keras, or Fast.ai. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 **Who Is the Company Behind Universal Data Tool?** - **Seller:** [Universal Data Tool](https://www.g2.com/sellers/universal-data-tool) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 8. [Watchful](https://www.g2.com/products/watchful/reviews) Watchful is a modern and interactive solution that places the control of data labeling back into the hands of data scientists and subject matter experts. Through our scalable data-centric approach, anyone can holistically explore, classify, annotate and validate any unique dataset to power today’s AI initiatives and business processes. Watchful’s enterprise-ready solution removes the data bottlenecks associated with AI from the start, allowing for the iterative processes of AI, from production to deployment, to be far more cost-effective and scalable. Use Watchful across multiple industries such as manufacturing, retail, finance, life sciences, and more. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 **How Do G2 Users Rate Watchful?** - **Labeler Quality:** 10.0/10 (Category avg: 8.9/10) - **Object Detection:** 8.3/10 (Category avg: 8.9/10) - **Data Types:** 8.3/10 (Category avg: 8.8/10) - **Ease of Use:** 8.3/10 (Category avg: 8.8/10) **Who Is the Company Behind Watchful?** - **Seller:** [Watchful.io](https://www.g2.com/sellers/watchful-io) - **Year Founded:** 2018 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/15227854 (6 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business ### 9. [ActiveNav Data Expert's ToolKit](https://www.g2.com/products/activenav-data-expert-s-toolkit/reviews) ACTIVE-Governance provides a continuous monitoring of connected repositories and automatically applies policies to your content. The software notifies information owners and relevant users for action as part of a repeatable and defensible process. **Who Is the Company Behind ActiveNav Data Expert's ToolKit?** - **Seller:** [Active Navigation](https://www.g2.com/sellers/active-navigation) - **Year Founded:** 2008 - **HQ Location:** Reston, US - **Twitter:** @ActiveNav (691 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/active-navigation/ (38 employees on LinkedIn®) ### 10. [AI Data Collection Company](https://www.g2.com/products/ai-data-collection-company/reviews) Globose Technology Solutions (GTS) is an AI data collection company with over 25 years of experience in the industry. GTS specializes in providing high-quality datasets tailored for machine learning applications, including image, video, speech, and text datasets. Their comprehensive services encompass meticulous data labeling, streamlined data operations, efficient production pipelines, and the integration of human-in-the-loop methodologies to ensure impeccable results. Key Features and Functionality: - Diverse Data Collection: GTS offers specialized datasets such as medical imagery, invoices, facial recognition images, CCTV footage, traffic videos, and speech data for natural language processing projects. - Comprehensive Data Annotation: They provide various annotation techniques, including image and video annotation, audio transcription, and text data labeling, enhancing the quality and usability of datasets. - Global Reach: With a workforce located in 136 countries and offices in the USA, China, and India, GTS ensures diverse and extensive data collection capabilities. - Quality Assurance: GTS adheres to rigorous quality assurance measures and holds certifications such as ISO 9001:2015 and ISO/IEC 27001:2013, ensuring high standards in data collection and management. Primary Value and Solutions: GTS addresses the critical need for high-quality, diverse datasets essential for training accurate and efficient AI and machine learning models. By offering tailored data collection and annotation services, GTS enables organizations to enhance their AI capabilities, reduce human errors, and improve productivity across various sectors, including manufacturing, customer service, and logistics. **Who Is the Company Behind AI Data Collection Company?** - **Seller:** [AI Data Collection Company](https://www.g2.com/sellers/ai-data-collection-company) - **Year Founded:** 2014 - **HQ Location:** Bhiwadi, IN - **LinkedIn® Page:** https://linkedin.com/company/gtsaidata (72 employees on LinkedIn®) ### 11. [alignerr AI](https://www.g2.com/products/alignerr-ai/reviews) Alignerr AI is a cutting-edge platform designed to streamline and enhance the process of training artificial intelligence models across various domains. By leveraging a network of specialized trainers, Alignerr AI ensures that AI systems are developed with high-quality, domain-specific data, leading to more accurate and reliable outcomes. Key Features and Functionality: - Domain-Specific Training: Alignerr AI connects AI trainers with expertise in specific fields, such as electrical engineering, microbiology, and various languages, to provide tailored training data. - Remote Collaboration: The platform offers freelance, remote opportunities, enabling trainers from around the world to contribute to AI development without geographical constraints. - Quality Assurance: By employing experts in their respective fields, Alignerr AI ensures that the training data is accurate, relevant, and of high quality, which is crucial for the performance of AI models. Primary Value and User Solutions: Alignerr AI addresses the challenge of obtaining high-quality, domain-specific training data for AI models. By facilitating collaboration between AI developers and subject matter experts, the platform enhances the accuracy and reliability of AI systems. This approach not only improves the performance of AI applications but also accelerates the development process by providing readily available, expert-verified data. For organizations and developers seeking to build robust AI solutions, Alignerr AI offers a streamlined pathway to access specialized knowledge and training resources. **Who Is the Company Behind alignerr AI?** - **Seller:** [alignerr AI](https://www.g2.com/sellers/alignerr-ai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/alignerr/ (1,709 employees on LinkedIn®) ### 12. [APISCRAPY](https://www.g2.com/products/apiscrapy/reviews) APISCRAPY is an AI-driven web scraping and automation tool that converts any web data into ready-to-use data API. The tool is capable to extract data from websites, process data, automate workflows, classify data and integrate ready-to-consume data into database or deliver data in any desired format. Our customers leverage APISCRAPY tool for building AI products and services, data labeling, data annotation, business intelligence, market research, price monitoring, data aggregation, lead generation, brand protection, robotic process automation, and more. Key Benefits: -Converts any web & app data into ready-to-use data API -AI-augmented & pre-built automation capabilities -Pre-built data classification capabilities -Real-time or schedule data with intuitive dashboards -Ready-built database integrations capabilities -No coding, no infrastructure investment -Outcome-based pricing Other Tools from AIMLEAP: AI-Labeler: AI-augmented annotation & labeling tool. AI-Labeler is an AI-augmented data annotation platform that combines the power of artificial intelligence with in-person involvement to label, annotate and classify data, allowing faster development of robust and accurate models. AI-Data-Hub: On-demand data for building AI products & services. On-demand AI data hub for curated data, pre-annotated data, pre-classified data, and allowing enterprises to obtain easily and efficiently, and exploit high-quality data for training and developing AI models. PRICE-SCRAPY: AI-enabled real-time pricing tool An AI and automation-driven price solution that provides real-time price monitoring, pricing analytics, and dynamic pricing for companies across the world.  API-KART: AI driven data API solution hub  API-KART is a data hub that allows businesses and developers to access and integrate large-volume data from various sources. It is a data solution hub for accessing data through APIs, allowing companies to leverage data, and integrate APIs into their systems and applications. About AIMLEAP AIMLEAP is an ISO 9001:2015 and ISO/IEC 27001:2013 certified global technology consulting and service provider offering AI-augmented Data Solutions, Data Engineering, Automation, IT Services, and Digital Marketing services. AIMLEAP has been recognized as ‘The Great Place to Work®’. With a focus on AI and automation, we built quite a few AI & ML solutions, AI-driven web scraping solutions, AI-data Labelling, AI-Data-Hub, and Self-serving BI solutions. We started in 2012 and successfully delivered projects in IT & digital transformation, automation-driven data solutions, and digital marketing for more than 750 fast-growing companies in the USA, Europe, New Zealand, Australia, Canada; and more. -An ISO 9001:2015 and ISO/IEC 27001:2013 certified -Served 750+ customers -11+ Years of industry experience -98% Client Retention -Great Place to Work® Certified -Global Delivery Centers in the USA, Canada, India & Australia Locations: USA: 1-30235 14656 Canada: +1 4378 370 063 India: +91 810 527 1615 Australia: +61 402 576 615 Email: sales@aimleap.com To Visit APISCRAPY's Web Datastore Copy this URL & paste it into your browser:- www.apiscrapy.mydatastorefront.com **Average Rating:** 4.0/5.0 **Total Reviews:** 7 **How Do G2 Users Rate APISCRAPY?** - **Ease of Use:** 6.7/10 (Category avg: 8.8/10) **Who Is the Company Behind APISCRAPY?** - **Seller:** [AIMLEAP](https://www.g2.com/sellers/aimleap-6e7a8a8e-d612-4a86-a145-f57dc0cb066e) - **Year Founded:** 2012 - **HQ Location:** United States, US - **Twitter:** @aimleap (49 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2790423/admin/%20 (116 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 57% Enterprise, 29% Mid-Market #### What Are APISCRAPY's Pros and Cons? **Pros:** - AI Technology (1 reviews) - Analytics (1 reviews) - API Integration (1 reviews) - Automation (1 reviews) - Cloud Storage (1 reviews) **Cons:** - Difficult Learning (1 reviews) - Expensive (1 reviews) ### 13. [Bank Statement Extractor](https://www.g2.com/products/bank-statement-extractor/reviews) Bank Statement Extractor is an AI-powered platform designed to automate the conversion of PDF bank statements into structured Excel files, eliminating the need for manual data entry. Users can upload their bank statements, define the data they wish to extract, and receive accurate Excel files within seconds. The service supports multiple PDFs simultaneously, handles statements from any bank worldwide, and offers multi-language extraction capabilities. With a processing speed of over 1,000 transactions per minute and an accuracy rate of 99.8%, it significantly reduces the time and errors associated with manual data entry. Additionally, the platform ensures maximum privacy by processing files securely and deleting them immediately after extraction. Key Features and Functionality: - Custom Data Extraction: Users can specify the exact data fields they need, allowing for tailored Excel outputs. - Batch Processing: The platform supports the upload and processing of multiple PDF statements at once, regardless of the number of pages. - High Accuracy and Speed: Achieves 99.8% accuracy in data extraction and processes over 1,000 transactions per minute. - Multi-Language Support: Capable of extracting data from bank statements in various languages. - Data Privacy: Ensures user data security by processing files in memory and deleting them immediately after extraction. Primary Value and Problem Solved: Bank Statement Extractor addresses the inefficiencies and errors associated with manual data entry of bank statements. By automating the extraction process, it saves financial teams approximately 10-12 hours per week, allowing them to focus on more strategic tasks. The platform's high accuracy reduces the risk of human error, ensuring reliable financial data for analysis and reporting. Its flexibility in handling various bank formats and languages makes it a versatile tool for businesses operating globally. **Who Is the Company Behind Bank Statement Extractor?** - **Seller:** [Bank Statement Extractor](https://www.g2.com/sellers/bank-statement-extractor) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 14. [Cinder](https://www.g2.com/products/cinder/reviews) Cinder a fully-featured platform for AI Governance, Trust & Safety, and the adjudication of any content-based decision process at scale. If you're managing digital harms on a marketplace, social, or gaming platform; training bespoke AI; or setting parameters and benchmarks for consumer-facing AI, Cinder was built for you. Cinder includes a full suite of integrated tools, including: a comprehensive workflow engine that allows users to combine signals from classifiers, metadata, and content; best-in-class human labeling systems to drive accurate data collection and real-time operational decisions; and a quality assurance module to benchmark both AI and human flows. Cinder was built for adversarial environments, which means it is both powerful and easily configurable in the UI. Cinder's founding team built digital tools to keep pace with nation state attackers and terrorist groups - environments where iteration and adaptation happen fast. If you're facing adversarial threats or a competitive environment that demands constant product adaptation, Cinder was built to keep pace. **Who Is the Company Behind Cinder?** - **Seller:** [Cinder](https://www.g2.com/sellers/cinder) - **Year Founded:** 2021 - **HQ Location:** United States, US - **LinkedIn® Page:** https://www.linkedin.com/company/cinder-intelligence/ (26 employees on LinkedIn®) ### 15. [CommentEasy](https://www.g2.com/products/commenteasy/reviews) CommentEasy is an intuitive image annotation tool designed to streamline visual feedback for design teams. By enabling users to upload images, add precise comments, and share them effortlessly, it eliminates the inefficiencies of traditional feedback methods. With features like voice notes and link-based sharing, CommentEasy ensures clear, actionable communication without the need for sign-ups or additional software. Key Features and Functionality: - Image Annotation: Upload or paste images and add pinpointed comments directly on them. - Voice Notes: Leave voice annotations to convey feedback more naturally and effectively. - Link-Based Sharing: Share annotated images via a simple link, allowing recipients to view and respond without creating an account. - Version Control: Maintain clarity by keeping comments tied to specific versions of designs, reducing confusion during revisions. - No Sign-Ups Required: Facilitate seamless collaboration without the hassle of account creation for reviewers. Primary Value and Problem Solved: CommentEasy addresses the common challenges design teams face with scattered feedback, unclear communication, and prolonged revision cycles. By centralizing feedback on a single platform and offering both visual and verbal annotation tools, it enhances clarity and accelerates the design process. This leads to faster approvals, reduced miscommunication, and more efficient project timelines. **Who Is the Company Behind CommentEasy?** - **Seller:** [CommentEasy](https://www.g2.com/sellers/commenteasy) - **Year Founded:** 2025 - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/commenteasy/ (1 employees on LinkedIn®) ### 16. [CVAT Image & Video Annotation Solutions for AWS by Yobitel](https://www.g2.com/products/cvat-image-video-annotation-solutions-for-aws-by-yobitel/reviews) Amazon is a global e-commerce and cloud computing company founded in 1994 and headquartered in Seattle, Washington. The company operates through three main segments: North America, International, and Amazon Web Services (AWS). Amazon's retail platform offers millions of products across numerous categories through websites including amazon.com, amazon.ca, amazon.fr, amazon.de, and many others worldwide. The company manufactures electronic devices such as Kindle e-readers, Fire tablets, Fire TVs, and Echo smart speakers. Amazon provides services including AWS cloud computing, Kindle Direct Publishing for authors, marketplace platforms for third-party sellers, digital content streaming, and Amazon Prime membership program offering benefits like free shipping and media streaming. The company serves diverse customer segments including consumers, merchants, content creators, and enterprise clients across global markets. **Who Is the Company Behind CVAT Image & Video Annotation Solutions for AWS by Yobitel?** - **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud (2,229,345 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®) - **Ownership:** NASDAQ: AMZN ### 17. [Deepen 4D](https://www.g2.com/products/deepen-4d/reviews) Deepen 4D is an advanced data annotation and calibration platform designed to enhance the development of autonomous systems and robotics. It offers a comprehensive suite of tools and services that streamline the processes of labeling, calibrating, and validating multi-sensor data, ensuring high accuracy and efficiency in machine learning and AI applications. Key Features and Functionality: - Annotation Tools: Provides AI-powered 2D and 3D annotation capabilities, including bounding boxes, semantic segmentation, and point cloud labeling. Features such as one-click bounding boxes and machine learning-assisted object detection and tracking significantly improve labeling speed and accuracy. - Calibration Suite: Offers multi-sensor calibration solutions that support various sensor types, including LiDAR, cameras, radar, and IMU. The platform enables users to calculate intrinsic and extrinsic calibration parameters swiftly, with advanced visualization features for precise calibration accuracy assessment. - Validation Mechanisms: Includes automated quality checks to identify common labeling issues, collaborative feedback loops for annotation refinement, and comprehensive issue management systems to track and resolve data inconsistencies. Primary Value and User Solutions: Deepen 4D addresses the critical need for precise and efficient data annotation and calibration in the development of autonomous systems. By automating and streamlining these processes, it reduces the time and resources required for data preparation, allowing organizations to focus on innovation and deployment. The platform's robust quality assurance measures ensure the reliability of annotated data, which is essential for the safety and performance of AI-driven applications. Additionally, its scalability and flexibility make it suitable for enterprises and startups alike, facilitating the advancement of autonomous technologies across various industries. **Who Is the Company Behind Deepen 4D?** - **Seller:** [Deepen 4D](https://www.g2.com/sellers/deepen-4d) - **Year Founded:** 2017 - **HQ Location:** San Jose, California, United States - **LinkedIn® Page:** https://www.linkedin.com/company/deepen-ai (207 employees on LinkedIn®) ### 18. [Fixpoint](https://www.g2.com/products/fixpoint/reviews) Fixpoint is a comprehensive solution designed to streamline human data operations for AI data companies. By automating the sourcing, vetting, and management of expert annotator teams, Fixpoint enables organizations to rapidly scale their workforce while ensuring quality and compliance. Key Features and Functionality: - White-Glove Expert Staffing: Fixpoint can source and hire qualified experts across various domains, assembling teams of hundreds within weeks. - Worker Vetting API: This API automates the verification of candidates' education, credentials, and backgrounds, delivering results in minutes and identifying up to ten times more fraudulent applicants compared to manual reviews. - Diverse Expertise: Fixpoint staffs expert teams in fields such as legal, coding, medical, STEM, and linguistics, tailoring to specific project requirements. - Global Coverage and Compliance: With operations spanning multiple regions, Fixpoint ensures adherence to regulations like GDPR and is pursuing SOC 2 certification. Primary Value and Solutions Provided: Fixpoint addresses the challenges AI data companies face in rapidly scaling expert annotator teams without compromising quality or compliance. By automating the sourcing and vetting processes, Fixpoint reduces the time and cost associated with manual recruitment and verification. This allows organizations to focus on delivering high-quality training data, confident in the qualifications and reliability of their workforce. **Who Is the Company Behind Fixpoint?** - **Seller:** [Fixpoint](https://www.g2.com/sellers/fixpoint) - **Year Founded:** 2023 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/gofixpoint (6 employees on LinkedIn®) ### 19. [Foresight Training Data](https://www.g2.com/products/foresight-training-data/reviews) Lightning Rod Labs is the data engine behind the next generation of AI models and products, giving builders the tools to transform messy, real-world data into model-ready training datasets. Teams use Foresight Data to automatically generate label datasets from both public sources and their own proprietary data. Eliminating manual annotation and accelerating AI workflows from prototyping to deployment. From AI startups to Enterprise research teams, builders rely on Lightning Rod Labs to turn complex, unstructured data into a durable competitive advantage. We’re making training data as automated, scalable, and adaptive as the models and products it powers. **Who Is the Company Behind Foresight Training Data?** - **Seller:** [Lightning Rod Labs](https://www.g2.com/sellers/lightning-rod-labs) - **HQ Location:** New York City, US - **LinkedIn® Page:** https://www.linkedin.com/company/lightningrod/ (6 employees on LinkedIn®) ### 20. [Frekil](https://www.g2.com/products/frekil/reviews) Accelerate Real World Evidence Generation from months to minutes for internal hypothesis testing from your own data **Who Is the Company Behind Frekil?** - **Seller:** [Frekil](https://www.g2.com/sellers/frekil) - **Year Founded:** 2026 - **HQ Location:** San-Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/frekil/ (3 employees on LinkedIn®) ### 21. [Getmarkup](https://www.g2.com/products/getmarkup/reviews) GetMarkup is an advanced online annotation tool designed to transform unstructured text into structured data, facilitating natural language processing (NLP) and machine learning (ML) applications. By leveraging the capabilities of GPT-4, it streamlines the annotation process, offering predictive suggestions that enhance workflow efficiency and reduce manual effort. Key Features and Functionality: - AI-Powered Annotations: Utilizes GPT-4 to provide predictive annotation suggestions, accelerating the data structuring process. - Ontology Integration: Supports both standard and custom ontologies, enabling precise and context-aware annotations. - User-Friendly Interface: Designed with an intuitive interface, making it accessible to users with varying levels of technical expertise. - Scalability: Offers flexible pricing plans to accommodate projects of all sizes, from individual users to large organizations. - Data Security: Implements industry-standard encryption protocols and strict access controls to ensure data privacy and security. Primary Value and Problem Solved: GetMarkup addresses the challenge of converting unstructured text into structured data, a critical step in NLP and ML projects. By automating and enhancing the annotation process, it significantly reduces the time and effort required for data preparation, leading to more efficient project workflows and improved model accuracy. Its integration with various ontologies ensures that annotations are contextually relevant, thereby enhancing the quality of the structured data produced. **Who Is the Company Behind Getmarkup?** - **Seller:** [Markup](https://www.g2.com/sellers/markup-8f4a3328-b326-4829-b3a6-18f2e38fb22d) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 22. [Humanloop](https://www.g2.com/products/humanloop/reviews) Humanloop is the LLM evals platform for enterprises. Teams at Gusto, Vanta and Duolingo use Humanloop to ship reliable AI products. We enable you to adopt best practices for prompt management, evaluation and observability. **Who Is the Company Behind Humanloop?** - **Seller:** [Humanloop](https://www.g2.com/sellers/humanloop) - **Year Founded:** 2020 - **HQ Location:** London, GB - **Twitter:** @humanloop (9,718 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/humanloop/ (13 employees on LinkedIn®) ### 23. [Intellabel](https://www.g2.com/products/intellabel/reviews) Intellabel is an advanced data labeling platform designed to streamline the process of annotating large datasets for machine learning applications. By leveraging artificial intelligence and automation, Intellabel significantly reduces the time and effort required for data labeling, enabling organizations to accelerate their AI development cycles and improve model accuracy. Key Features and Functionality: - Automated Labeling: Utilizes AI algorithms to automatically annotate data, minimizing manual intervention and increasing efficiency. - Customizable Workflows: Offers flexible workflows that can be tailored to specific project requirements, ensuring adaptability across various use cases. - Quality Control Mechanisms: Implements robust quality assurance processes to maintain high annotation accuracy and consistency. - Scalability: Capable of handling large-scale datasets, making it suitable for enterprises with extensive data needs. - Integration Capabilities: Seamlessly integrates with existing machine learning pipelines and tools, facilitating a smooth workflow. Primary Value and Problem Solved: Intellabel addresses the challenge of time-consuming and labor-intensive data labeling by providing an automated and efficient solution. This enables organizations to focus more on developing and deploying machine learning models rather than spending excessive time on data preparation. By improving the speed and accuracy of data annotation, Intellabel helps businesses accelerate their AI initiatives and achieve better outcomes. **Who Is the Company Behind Intellabel?** - **Seller:** [Intellabel](https://www.g2.com/sellers/intellabel) - **Year Founded:** 2019 - **HQ Location:** Bangalore, IN - **LinkedIn® Page:** https://www.linkedin.com/company/intellabel/ (81 employees on LinkedIn®) ### 24. [Isahit](https://www.g2.com/products/isahit/reviews) Isahit is an ethical on-demand workforce management platform that specializes in scaling AI and data projects through human-in-the-loop processes. By providing services such as data labeling, natural language processing, image and video annotation, and data processing, Isahit ensures high-quality, bias-free AI development. The platform uniquely combines technological expertise with social impact by empowering women in developing countries, offering them flexible digital work opportunities and bridging the digital divide. Key Features and Functionality: - Data Labeling and Annotation: Offers comprehensive services in image, video, and text annotation to train AI models effectively. - Natural Language Processing (NLP): Provides tools for tasks like named entity recognition and text classification, enhancing language model capabilities. - Data Processing Services: Assists with tasks such as data entry, cleaning, and management, streamlining back-office operations. - Human-in-the-Loop (HITL) Integration: Ensures AI models are fine-tuned with human oversight, improving accuracy and reducing biases. - Ethical Workforce Management: Empowers women across multiple continents by offering flexible, remote digital work opportunities, promoting social inclusion and financial independence. Primary Value and User Solutions: Isahit addresses the critical need for high-quality, unbiased data in AI development by integrating human expertise into the data processing pipeline. This approach not only enhances the accuracy and fairness of AI models but also provides scalable solutions for businesses across various sectors, including automotive, healthcare, finance, and e-commerce. Additionally, by focusing on ethical outsourcing, Isahit contributes to social impact by creating meaningful employment opportunities for women in developing countries, thereby fostering economic empowerment and bridging the digital divide. **Who Is the Company Behind Isahit?** - **Seller:** [Isahit](https://www.g2.com/sellers/isahit) - **Year Founded:** 2017 - **HQ Location:** Paris, FR - **LinkedIn® Page:** https://fr.linkedin.com/company/isahit (285 employees on LinkedIn®) ### 25. [jpgtotext.com](https://www.g2.com/products/jpgtotext-com/reviews) An Image to Text Converter is an online OCR (Optical character recognition) tool that extracts text from images and converts it into editable text. This means you can easily copy, paste, and edit text from images, such as JPGs and PNGs, without the hassle of manual typing. **Who Is the Company Behind jpgtotext.com?** - **Seller:** [jpgtotext.com](https://www.g2.com/sellers/jpgtotext-com) - **Year Founded:** 2021 - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/showcase/jpgtotext-com (1 employees on LinkedIn®) ## What Is Data Labeling Software? [Artificial Intelligence Software](https://www.g2.com/categories/artificial-intelligence) ## What Software Categories Are Similar to Data Labeling Software? - [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) - [MLOps Platforms](https://www.g2.com/categories/mlops-platforms) - [Active Learning Tools](https://www.g2.com/categories/active-learning-tools) --- ## How Do You Choose the Right Data Labeling Software? ### What You Should Know About Data Labeling Software ### What is Data Labeling Software? Data labeling software labels or annotates data for training machine learning models. Machine learning algorithms rely on large amounts of labeled data to learn patterns and make predictions. Data labeling solutions help humans identify and label the relevant features and characteristics of the data that will be used to train the machine learning model. Many types of data labeling solutions are available, ranging from simple tools that allow users to label data manually to more advanced tools that use machine learning algorithms to automate the labeling process. Some data labeling software also includes features such as image annotation tools, which allow users to label and annotate images and other visual data. Data labeling software is used in various applications, including[](https://www.g2.com/articles/natural-language-processing)[natural language processing,](https://www.g2.com/articles/natural-language-processing) image and video classification, and[](https://www.g2.com/articles/object-detection)[object detection](https://www.g2.com/articles/object-detection). It is an important tool in the development and training of machine learning models and plays a critical role in their accuracy and effectiveness. ### What types of data labeling software exist? Selecting a data labeling software requires a prior evaluation and understanding of data-driven workflows in your business. Below are the types of software you can consider. - **Manual labeling software:** These data labeling platforms segment, label, and classify data with the help of a "[human in the loop"](https://www.g2.com/glossary/human-in-the-loop-definition) service. Human annotators label the training data based on businesses' geographic locations. The data annotation service is extended to the[ML model](https://www.g2.com/articles/machine-learning-models) development workflow, and labeling data becomes more effective. - **Automated labeling software:** The automated data labeling software preprocesses raw datasets consisting of text, images, liDAR data, DICOM, PDF, or audio using an unsupervised learning approach. The algorithm assigns labels and categories to data without referring to external annotators. - **Active learning labeling software:** Also known as active learning tools, these are semi-supervised tools that follow a "query-based" approach to labeling data. Based on the uncertainty score, they query data using manual or annotator labeling. For more challenging labels, they prompt the human annotator with queries. - **Crowdsource labeling software:** These data labeling platforms crowd data labeling services to a crowd of developers to[train high-quality data pipelines](https://learn.g2.com/training-data). Custom data labeling can be ideal for large or enterprise-sized teams. - **Integrated labeling and model training software:** These tools provide combined services for data labeling and predictive modeling. Using advanced data analysis, users can label, train, and build machine learning models to optimize their production cycles. ### What are the Common Features of Data Labeling Software? There are several features that are often included in data labeling software, including: - **Label assignment:** Data labeling software allows users to assign labels or tags to specific data points, such as text, images, or videos. - **Annotation tools:** Some data labeling software includes tools for annotating data, such as bounding boxes, polygon drawing tools, cloud points, keymakers, and point annotation tools. These tools can be used to highlight specific features or characteristics of the data. - **Machine learning algorithms:** Some data labeling software uses machine learning algorithms to automate the labeling process or generate initial labels for data, which humans can then review and correct as needed. - **Data management and organization** : Data labeling software often includes features for organizing and managing large datasets, such as the ability to filter and search for specific data points, track progress and completion, and generate reports. - **Collaboration tools:** Some data labeling software includes collaboration tools, such as the ability to assign tasks to multiple users, track changes and revisions, and review and discuss data labeling decisions. - **Integration with data science and machine learning platforms** : Some data labeling software is designed to integrate with popular[](https://www.g2.com/categories/data-science-and-machine-learning-platforms)[data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), such as TensorFlow or PyTorch, making it easier to use the labeled data to train machine learning models. - **Image, text, audio, or video annotation:** These tools comply with multiple unstructured data formats to train and validate models designed to generate output in images, text, video, audio, PDF, and so on. ### Benefits of Data Labeling Software Choosing a data labeling platform empowers businesses to either pre-train existing machine learning models to save time or build new models to upgrade their workflows and train teams.  While data labeling platforms can help do both, it also has some significant benefits listed as under: - **Improved accuracy and quality of labeled data** : Data labeling software can help ensure that data is accurately and consistently labeled, which is critical for the accuracy and effectiveness of machine learning models. - **Increased efficiency and productivity** : Data labeling software can help streamline the data labeling process, allowing users to label more data in less time. This can be particularly useful for large datasets or repetitive or routine tasks. - **Enhanced collaboration and team communication:** Some data labeling software includes collaboration tools, such as the ability to assign tasks to multiple users and track changes and revisions. These tools can help improve communication and coordination within teams working on data labeling projects. - **Reduced cost** : Using data labeling software can help reduce the cost of data labeling projects by automating routine tasks and reducing the need for manual labor. - **Increased flexibility and scalability** : Data labeling software can be used to label a wide variety of data types and can be easily scaled up or down as needed to meet project demands. - **Respite for data operations, ML, and data science teams:** These solutions offer agile service marketplaces with high-quality labelers and annotators that solve the problems of data cleaning, preprocessing, and classification for these teams. - **Superpixel segmentation and brushes:** These tools are also widely used for image recognition, natural language processing (NLP), and computer vision algorithms. It creates region pools using brushing and superpixel segmentation to classify images. ### Who Uses Data Labeling Software? The data labeling tools are a must-have for businesses that want to foray into AI automation and build robust and efficient product applications and SDK with pre-installed machine learning capabilities. Below are the individuals and organizations that use data labeling platforms: - **Data scientists and machine learning engineers** : Data scientists and machine learning engineers use data labeling software to label and annotate data that will be used to train machine learning models. This helps the models learn to recognize patterns and make predictions based on the labeled data. - **Business analysts and data analysts** : Business analysts and data analysts may use data labeling software to label and annotate data to create reports and visualizations or for use in machine learning models. - **Quality assurance professionals** : Quality assurance professionals may use data labeling software to label and annotate data to test and debug machine learning models or other software applications. - **Researchers** : Researchers in various fields, such as computer science, linguistics, and biology, may use data labeling software to label and annotate data to conduct research or develop machine learning models. ### Alternatives to data labeling software Some alternatives to data labeling software provide annotation and labeling services along with other machine learning features. - [Natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) **:** The NLP software derives semantic relationships between words of an input sentence and generates relevant and personalized content. These tools replicate the functioning of a human brain to register prompt intent and derive coherent content blocks. - [Machine learning operationalization (MLOps software):](https://www.g2.com/categories/mlops-platforms) The MLOPs software facilitates the entire machine learning model journey, from data preprocessing to ML integration and delivery. It applies various DevOps automation concepts and runs ML-based workflows without human supervision. - [Image recognition software:](https://www.g2.com/categories/image-recognition) Image recognition software detects, categorizes, and localizes digital images or photographs. It is based on specialized deep-learning models that group data into grids and identify relevant categories of all objects. ### Challenges with Data Labeling Software Even though data labeling software reduces costs, provides security and privacy to data, and moderates data quality control, some evident challenges can occur at any stage of working with this platform. Below are some of the challenges of data labeling software - **Data quality and consistency:** It is not certain that data labeling tools would predict accurate labels for ML models. Sometimes, the platform can incorrectly categorize text as video or process incorrect calculations, which can lower the data quality. - **Scalability:** As a business receives large influxes of data, repurposing raw data to train models, make model versions, calculate risks, and be consistent with quality control becomes a challenge and results in scalability problems for different teams across the company. - **Cost: ** Though data labeling platforms tend to be cheaper than other expensive human annotation services, submitting a large cluster of datasets for categorization can become costly. It would exhaust your credits and leave you with no alternative but to upgrade to a more expensive plan. - **Complexity of tasks:** Not all data labeling tasks are simple. Some require deep domain exercises and more specialized algorithm training, such as reinforcement learning, query sampling, or entropy, to build ML models accurately without investing in external annotation services. - **Data privacy and security:** These platforms are open source or paid. However, they retrieve and store data on[](https://www.g2.com/categories/hybrid-cloud-storage-solutions)[hybrid](https://www.g2.com/categories/hybrid-cloud-storage-solutions) or[](https://www.g2.com/articles/public-cloud)[public cloud storage platforms](https://www.g2.com/articles/public-cloud), which can infect your dataset and give hackers and fishers leeway to infect the data.  ### What companies should buy data labeling software? Companies that want to optimize the quality of their datasets and build powerful algorithms should consider data labeling software. Not just because it helps label data but because it can build accurate predictions and forecasts. Here are some companies that can benefit from these tools: - **Machine learning startups or research labs:** These companies conduct the majority of machine learning experiments and constantly work with data tools. Investing in a data labeling tool can benefit their AI research and ML model development processes. - **Data companies:** Companies that provide data management services like search engines, e-commerce platforms, or social media management tools also need data labeling software to generate effective algorithms that generate accurate responses and deal with large data volumes. - **Market research companies:** Companies that conduct market research or gather customer insights and trends can also benefit from data labeling platforms. These platforms allow them to gather real-time market trends and track consumer behaviors. - **Healthcare organizations:** These companies utilize data labeling platforms for early detection of diseases, medical imaging, patient recordkeeping, consultation, and treatments. With this software, they accurately study patient data and forecast treatment cycles. ### How to Buy Data Labeling Software Investing in data labeling software is a step-by-step process that requires the input of all related teams and stakeholders. Below are the steps buyers need to follow chronologically to purchase the best data labeling platform for their business.  #### Requirements Gathering (RFI/RFP) for Data Labeling Software Before purchasing, buyers should consider their needs and determine what they hope to achieve with this software. Evaluate the type of database system, products, AI maturity, and budget data from revenue teams. Also, make a list of the data-related and language services you expect from the product. Enlist all these points in the form of a structured request for proposal (RFP) and get the approval of your teams and stakeholders who are involved in the decision-making process. #### Compare Data Labeling Software Products Evaluate the shortlisted products' features, security and privacy guidelines, pros and cons, pricing, and AI functionalities. Compare the features and benefits with the requirements your team has listed in the request for proposal. Analyze the budget, contract metrics, and return on investment for each software feature and compare them with those of other contenders in the market.  At this stage, buyers can also request demos or free trials to see how the software works and ensure it meets their needs. While shortlisting vendors, it is also crucial to consider their credibility. Look for vendors with a strong track record and a good reputation. #### Selection of Data Labeling Software Discuss all shortlisted software's technical and configuration workflows with your IT and software development teams. Sit with them to analyze current software consumption, active subscription plans, system of records, and IT audit reports, and then check where this software fits in your tech stack. Discuss the compatibility of the software with related account executives and sales teams to ensure that the software doesn't cause more overheads and storage expenses for your teams. #### Negotiation After finalizing the software, get your legal teams to draft a legitimate contract outlining RFP terms, renewal policies, data retention and privacy policies, and the vendor's non-compete and discuss it with the vendor. At this stage, it is also feasible to negotiate for a better subscription rate, more features, or add-ons that buyers are interested in at the vendor's discretion.  #### Final decision The final decision to purchase data labeling software lies with the buyer's decision-making teams. These could be the chief information officer (CIO), head of the data science team, or procurement team. While making this decision, it is also important to consider budget constraints, team queries, or business objectives. It will be helpful to consult with stakeholders and experts, like data scientists and ML engineers, to get their input on the best data labeling solution for the institution. ### What does data labeling software cost? The cost of data labeling software can vary widely depending on its specific features and capabilities, as well as the size and scope of the deployment. Some software is free or open-source, while others are commercial products sold on a subscription or per-use basis. Data labeling software designed for enterprise-level use with a wide range of advanced features will be more expensive than straightforward solutions. Prices can range from a few hundred dollars per year for an introductory subscription to several thousand dollars for a more comprehensive solution. It is essential to evaluate subscription, license, pay-per-seat, and pay-per-token usage costs to check whether the product is suitable for your business and has scope for a decent return on investment (ROI). While you are engaged in the monetary calculations, factor in software upgrade cost, business size, version, software maintenance, and upsell costs to indicate the budget clearly. These tools can help improve productivity and efficiency, contributing to ROI calculation. To calculate the ROI of data labeling software, the following formula can be used: ROI = (Benefits - Costs) / Costs "Benefits" is the value of the time saved and increased productivity resulting from using the software, and "Costs" is the total cost of the software license and any additional costs associated with implementation and use. ### Implementation of data labeling software When considering purchasing data labeling software, companies should have a rough vision of how to implement it for data science and machine learning teams. Other factors, such as alignment with notebook editors, statistical tools, data analysis limitations, training, and testing ML cycles, will be altered and modified per the implementation timeline of data labeling software. Below are some tips to ensure a smooth implementation. - **Integration with existing data and ML workflows:** Consult your software development teams on setting up user permissions and integrating this platform with your existing code development platform, such as R or Python editors. The first step is to ensure it is compatible with various data formats, data types, data analysis tools, and other collaborative ML tools. - **Customization and flexibility in labeling tasks:** These platforms must be agile and compatible with datasets of multiple formats and languages. It should provide customization for various tasks such as image recognition, computer vision, audio generation, video generation, and[speech recognition](https://www.g2.com/glossary/speech-recognition-definition). Labeling unstructured data should be open to anyone who authenticates their identity through multi-factor authentication and is an authorized user. - **Collaboration and workforce management features:** The data labeling platform needs to be activated for model prototype and version control. It should have features like role-based access control, data privacy and security guidelines, user authentication, model collaboration, and ML code supervision. The platform should be accessible to respective team members so they can double-check the labeled tasks and stop the model from hallucinating at any stage of the training data pipeline. - **Quality assurance and review mechanisms:** When a model's output accuracy depends on the quality of training data, it is evident that data labeling platforms need to be set of modulation accuracy, quality control, and labeling review mechanisms. Given the models might inaccurately label datasets or predict wrong values, the labels need to be further supervised by a human in the loop service or external human oracle. - **Scalability, automation, and cost efficiency:** As labeling needs grow, ML engineers and developers need to invest in a scalable and cost-efficient data labeling solution that doesn't obstruct their network infrastructure and database architecture. The final implementation step is to ensure that the controls are set, the license is active, and the platform is retrieving and labeling data typically. ### Data Labeling Software Trends Overall, these trends reflect the growing importance of data labeling in the machine learning and AI ecosystem and the need for tools and technologies to help organizations create and manage large datasets of labeled data efficiently and effectively. There are several trends surrounding data labeling software that are worth noting: - **Increased adoption of artificial intelligence (AI) and machine learning (ML)**: One key trend in data labeling software is the increasing adoption of AI and ML technologies. Many software solutions now incorporate AI and machine learning algorithms to automate and streamline the data labeling process, improving efficiency and accuracy. As with general AI software,[](https://www.g2.com/articles/ai-trends-2023)[G2 expects this software to get cheaper](https://www.g2.com/articles/ai-trends-2023). - **Growing demand for high-quality labeled data** : Another trend is the growing demand for high-quality labeled data to train and test machine learning models. Data labeling software can help organizations create and manage large datasets of labeled data, improving the quality and reliability of machine learning models. - **Focus on user experience and collaboration** : Another trend in data labeling software is a focus on user experience and collaboration. Many data labeling software solutions now offer intuitive and user-friendly interfaces, tools, and features that facilitate collaboration and teamwork. _Researched and written by_ [_Matthew Miller_](https://learn.g2.com/author/matthew-miller)