Appen platform | The Essential High-Quality Data Annotation Platform
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Retail Case Studies and Success Stories with Appen platform
CASE STUDY A major global eCommerce company
Appen helped a major global eCommerce company speed up feature testing with ad-hoc evaluations. The company needed fast, flexible support for urgent evaluation projects that strained internal teams. ...Appen provided project management, a flexible platform, and access to qualified raters. Projects launched within two days and finished quickly, often in two to three days. The company scaled its evaluation process, kept quality high, and met tight deadlines using Appen's platform and services.
Appen helped a major global eCommerce company speed up feature testing with ad-hoc evaluations. The company needed fast, flexible support for urgent evaluation projects that strained internal teams. ...Appen provided project management, a large pool of qualified raters, and a platform for quick data uploads and results. The first project launched within two days and finished in another two, exceeding expectations. Over several months, the company completed ten more projects, each with fast turnaround and high quality. The client now tests website changes quickly and relies on Appen for ongoing ad-hoc evaluations.
Entertainment Case Studies and Success Stories with Appen platform
CASE STUDY A popular children's video platform
Appen helped a popular children's video platform improve search relevance and content filtering. The platform needed to keep children safe by filtering out inappropriate content. Appen quickly scaled... up a quality team to review and rate videos for age-appropriate material. The team checks tens of thousands of videos each month. They flag videos with hidden inappropriate content, making the platform safer for kids.
Appen’s Data Labeling Platform helped a top gaming company improve customer support with AI. The company faced challenges scaling their training data for chatbots and relied on manual Excel processes.... Appen’s platform allowed them to onboard more labelers, centralize data, and track performance. The team grew and increased labeled data from 13,000 to nearly 156,000 rows. This led to faster chatbot response times and a better player experience.
Appen’s data labeling platform helped a large US-based gaming company improve customer support. The company struggled to scale its AI chatbot training due to limited resources and manual processes. A...ppen’s platform replaced Excel-based workflows, allowing quick onboarding of new labelers and better tracking of performance. The team grew rapidly, increasing labeled data from 13,000 to nearly 156,000 rows. Chatbots now respond faster and more accurately, giving players a better support experience.
Appen helped a popular children's video platform improve search relevance and content filtering. The platform needed to keep children safe by filtering out inappropriate content. Appen quickly assemb...led a quality team and provided clear rating guidelines for age-appropriate content. The team reviews tens of thousands of video tasks each month. They flag videos with inappropriate voiceover content, making the platform safer for children.
Education Case Studies and Success Stories with Appen platform
CASE STUDY London School of Economics
Appen's data annotation platform helped the London School of Economics speed up data labeling for political science research. The LSE team replaced expert-only labeling with Appen's global crowd, red...ucing bias and increasing speed. A task that took weeks for experts was finished in four to five hours using the platform. The team labeled 20,000 sentences from six political parties and replicated studies in several languages. The solution enabled LSE to build machine learning models for political text analysis and improve research quality.
Appen Data Annotation Platform helped Johns Hopkins University study spider behavior. Researchers used AI to track millions of spider leg movements for behavioral neuroscience. The platform enabled f...ast, precise data labeling, saving over 1,500 hours of manual work. High-quality annotations trained machine vision models to analyze spider web-building. The project achieved low error rates in tracking algorithms, helping predict spider behavior patterns.
CASE STUDY Dr. Mark Harvey and the Larrakia Nation Aboriginal Corporation of People
Appen helped Dr. Mark Harvey and the Larrakia Nation Aboriginal Corporation of People preserve the Larrakia language. The challenge was to fix and link separate text and audio databases with many err...ors. Appen aligned the databases, enriched metadata, and added detailed phonetic annotation. This made the language data easier to use and more sustainable. The project aims to keep the Larrakia language alive for future generations.
CASE STUDY Dr. Mark Harvey and the Larrakia Nation Aboriginal Corporation of People
Appen helped Dr. Mark Harvey and the Larrakia Nation Aboriginal Corporation of People build a sustainable database to preserve the Larrakia language. The challenge was unlinked and error-prone text a...nd audio data. Appen aligned the databases, enriched metadata, and added detailed phonetic annotation. Their team provided English transcription, granular time-stamping, and speaker labeling. The result is a useable, long-lasting database to support language preservation and teaching.
Appen Data Annotation Platform helped Johns Hopkins University study spider behavior. Researchers used AI to track millions of spider leg movements during web building. The platform enabled fast, pre...cise data labeling, saving over 1,500 hours of manual work. High-quality annotations trained machine vision models to analyze spider posture. The project achieved low mean pixel errors in tracking algorithms, improving behavioral neuroscience research.
CASE STUDY London School of Economics and Political Science (LSE)
Appen's data annotation platform helped the London School of Economics (LSE) speed up and scale their data labeling for political science research. LSE researchers replaced slow, expert-only labeling... with Appen's global crowd, reducing bias and increasing speed. Tasks that took weeks for experts were finished in four to five hours. The team labeled 20,000 sentences from six political parties and replicated studies in several languages. Appen's platform enabled LSE to build models for analyzing political texts and improve research quality.
Non-Profit Organization Management Case Studies and Success Stories with Appen platform
CASE STUDY CLEAR Global
Appen helped CLEAR Global, a nonprofit, develop chatbots for mental health in Sheng, a Swahili-English slang spoken in Kenya. The challenge was Sheng's rapid evolution and lack of formal documentatio...n. Appen created a detailed Sheng language research document and a template for future language projects. Over two months, they delivered five consultation sessions and key resources. CLEAR Global can now build language research for new chatbot models in African languages.
Appen helped CLEAR Global develop chatbots for mental health in Sheng, a Swahili-English slang spoken in Kenya. The challenge was Sheng's rapid change and lack of formal documentation. Appen created ...a detailed Sheng Language Specific Peculiarities (LSP) document and a template for future research. Over two months, they delivered five consultation sessions and key resources. CLEAR Global can now create LSP documents for other languages and improve ASR models for African languages.
Marketing and Advertising Case Studies and Success Stories with Appen platform
CASE STUDY Shotzr
Appen helped Shotzr speed up image labeling for their marketing platform. Shotzr needed to identify which images required location metadata to improve search and recommendations. Using Appen's platfo...rm, Shotzr automated the process and quickly identified over 17,000 images that did not need extra labeling. They expect to remove over 61 million assets from manual review. This lets Shotzr focus on images that benefit from location data and build new models faster.
EmPushy used Appen's Data Annotation Services to improve user experience with push notifications. They needed a tool for multi-label annotation and GDPR-compliant data classification. Appen provided ...both Open Crowd and Dedicated Crowd contributors to categorize push notification CTAs and collect diverse, high-quality data. As a result, EmPushy increased their model's F1 score from 60% to over 85%. They processed over 200,000 campaigns and collected 223,000 judgments, helping them gain more clients.
EmPushy used Appen's data annotation services to improve push notification campaigns. They needed multi-label annotation and GDPR-compliant data for better audience targeting. Appen provided both Ope...n Crowd and Dedicated Crowd contributors to categorize push notification CTAs and collect diverse, high-quality data. EmPushy increased their model's F1 score from 60% to over 85%. They processed over 200,000 campaigns and collected 223,000 judgments, helping them gain more clients and publish research.
Appen’s crowdsourcing solution helped Zefr improve the quality and output of their data insights. Zefr needed to scale video data review for contextual advertising but struggled with limited internal... resources. By partnering with Appen, Zefr increased their video review capacity from 15,000 to 100,000 videos per month. This 6.6x increase let Zefr deliver faster, more reliable results to their customers. The quality and consistency of data also improved, reducing manual rework and boosting confidence in their solutions.
Appen helped GumGum improve how it annotates and classifies text and images for digital advertising. GumGum needed faster, high-quality data labeling to train its machine learning models. With Appen'...s ML-Assisted Data Annotation platform, GumGum can now annotate 10,000 rows of data in a few days or hours. This made their model development process 10 times faster. GumGum's data scientists now spend more time on research and less on manual annotation.
Appen helped Zefr improve the quality and output of their data insights. Zefr needed to scale their video data review process for contextual advertising. Appen's crowdsourcing solution gave Zefr acce...ss to more reviewers and better quality control. Zefr increased their video review rate from 15,000 to 100,000 videos per month. This allowed Zefr to deliver faster, more reliable results to their customers.
Appen helped Shotzr speed up image labeling for their marketing platform. Shotzr needed to quickly identify which images required location metadata. Using Appen's platform, Shotzr automated the proce...ss and improved their search relevance model. In just a few weeks, Shotzr identified over 17,000 images that did not need extra labeling. They expect to remove over 61 million assets from location data review, saving time and allowing focus on more valuable tasks.
Computer Software Case Studies and Success Stories with Appen platform
CASE STUDY A leading graphic design software company
Appen helped a leading graphic design software company improve its LLM image generator. The challenge was to create high-quality, culturally relevant images from text prompts in over 20 languages. Ap...pen localized prompts and evaluated AI-generated images for cultural fit and design quality. The team reviewed hundreds of outputs per language. This process led to better, more relevant designs and improved user satisfaction for the global software application.
Appen helped a leading creative software company improve their AI video description generator. The challenge was inaccurate and unclear AI-generated video descriptions. Appen used expert human valida...tion and automated checks to refine 40,000 video descriptions. The process achieved a 95% accuracy rate. The solution improved readability, coherence, and factual correctness at scale.
Appen Data Annotation Platform helped CallMiner process customer call data faster. CallMiner needed to analyze sentiment and emotion in large volumes of audio data. Manual annotation was slow and lim...ited their research. With Appen, CallMiner scaled up to tens of thousands of samples, improved accuracy, and saved time. The platform gave them better reporting and more diverse data perspectives.
Appen helped GumGum speed up data annotation for text and images. GumGum needed high-quality training data for its computer vision and NLP models in digital advertising. With Appen's ML-assisted data... annotation platform, GumGum could annotate 10,000 rows of data in a few days or hours, much faster than before. This let their data scientists focus on research instead of manual labeling. GumGum found the platform easy to use and support very responsive. The result was faster model development and better quality machine learning models.
Appen helped a leading creative software company improve their AI video description generator. The challenge was to fix errors and unclear language in AI-generated video descriptions. Appen used expe...rt human validation and automated checks to review and refine 40,000 video descriptions. This process achieved a 95% accuracy rate. The solution made the AI model more accurate, readable, and scalable.
CASE STUDY A major international software provider
Appen helped a major international software provider update its Unicode Common Locale Data Repository (CLDR) for 66 markets. The client needed in-market resources and strong project management to mee...t its yearly refresh cycle. Appen provided three resources per market, managed complex logistics, and handled data entry, voting, and forum participation. The client saved time and money, avoided sending employees into the field, and received a high-quality update. The project was a success and led to ongoing collaboration.
Microsoft Translator used Appen to expand its AI translation platform. Microsoft needed high-quality data for rare and less-spoken languages. Appen sourced and annotated language data from native spe...akers. Appen also addressed translation bias and ensured accuracy. As a result, Microsoft Translator now supports 110 languages, with Appen contributing to 108 of them.
CASE STUDY A leading graphic design software company
Appen helped a leading graphic design software company improve its LLM image generator. The challenge was to create high-quality, culturally relevant images from text prompts in over 20 languages. Ap...pen localized prompts and evaluated AI-generated images for cultural fit and design quality. Their two-step process included expert translation and detailed design review. The result was better, more relevant images and higher user satisfaction for the global software application.
CASE STUDY A major international software provider
Appen helped a major international software provider update its Unicode Common Locale Data Repository (CLDR) for 66 markets. The client needed in-market resources and strong project management to mee...t its yearly refresh cycle. Appen provided three resources per market, managed the project, and overcame challenges in sourcing talent. The client saved time and money by not using internal staff. The project led to a high-quality CLDR update and ongoing partnership.
Internet Case Studies and Success Stories with Appen platform
CASE STUDY A major social network provider
Appen helped a major social network provider improve its search functionality. The company needed better search for people, posts, and news, but its previous vendor could not meet quality needs. Appe...n started with a pilot using 80 US-based raters and quickly exceeded expectations. Within a year, the project grew to 1,200 raters across four markets and fifteen projects. The client now improves search faster and with higher quality data, using Appen’s expertise and global rater network.
Appen helped a major social network provider improve its search functionality. The company needed better search for people, posts, and news but struggled with a previous vendor. Appen ran a pilot wit...h 80 US-based raters, quickly ramping up to 1200 raters across four markets and fifteen projects in one year. The provider saw higher quality data, faster market entry, and a proven model for global expansion. Appen's expertise and remote rater model delivered cost-effective, efficient results.
Appen provided high-quality data collection for a leading social media company. The company needed large datasets to improve its machine learning model for understanding user messages. Appen recruite...d hundreds of participants and collected over one million samples in less than two months. The data included diverse language, slang, and idioms. The project helped the client release its product on time and control costs.
CASE STUDY A leading multilingual search engine and mobile app provider
Appen helped a leading multilingual search engine and mobile app provider improve its local business listings. The company struggled to keep listings accurate as more businesses appeared online. Appe...n used in-market evaluators to review and verify business data. The project grew from 10 to 440 evaluators in 31 markets. Over two years, they verified and corrected more than 750,000 business listings. This improved the user experience and data quality for the search engine.
Appen provided a leading social media company with high-quality training data to improve its machine learning model. The company needed large, diverse datasets to better understand user messages and ...meet a tight deadline. Appen recruited hundreds of participants and collected over one million samples in less than two months. This data helped the client release its product on time and improve features like the help center and ads. The project also allowed the company to control costs and benefit from diverse data sources.
Appen helped Microsoft Bing improve search quality in many markets. Bing needed better, more relevant search results for users worldwide. Appen quickly built and trained teams of data annotators and ...created tools for data analysis and reporting. They processed millions of search data pieces each month in over a dozen markets. Appen's support let Bing enter new markets fast and keep improving search quality.
Adobe Stock used the Appen platform to improve search relevance for over 200 million assets. The challenge was to help users find images with features like copy space, which were not captured in exis...ting metadata. Appen provided accurate training data by annotating images, highlighting areas suitable for text placement. This data powered new models, making it easier for users to find the right images quickly. The solution helped Adobe Stock deliver better results for marketers and creative professionals.
CASE STUDY Leading multilingual search engine provider
Appen provided in-market evaluators for a leading multilingual search engine provider to improve ad relevance. The provider needed accurate ad performance data across multiple international markets. ...Appen's team reviewed, labeled, and rated ads and their linked content using a five-point scale. The high-quality data helped the provider measure ad relevance, identify root causes of underperforming ads, and expand into new markets. The client saw increased ad relevance and higher revenue, and praised Appen for strong quality and timely delivery.
Adobe Stock used the Appen platform to improve search relevance for its huge image library. The challenge was to help users find images with features like copy space, which were not in the original m...etadata. Appen provided accurate training data by having people draw polygons over image areas best for text. This data helped Adobe build better models to surface the most useful images. Now, users can find the right images faster and create marketing materials more easily.
Appen helped Microsoft Bing improve search quality in many markets. Bing needed better, more relevant search results for users worldwide. Appen quickly built and trained teams to review and rate sear...ch data. They processed millions of search data pieces each month in over a dozen markets. Appen also created tools and training to support fast growth and high quality. This partnership let Bing expand quickly and improve search results for users.
Appen provided in-market evaluators to a leading search engine provider to improve ad relevance. The provider needed accurate ad performance data across multiple international markets. Appen's team r...eviewed, labeled, and rated ads and their linked content. This helped the provider measure ad relevance, identify root causes of underperforming ads, and expand into new markets. The high-quality data led to more relevant ads and higher revenue for the client.
CASE STUDY A leading multilingual search engine and mobile app provider
Appen helped a leading multilingual search engine and mobile app provider improve its local business listings. The company struggled to keep listings accurate as more businesses appeared online. Appe...n used in-market evaluators to review, verify, and update business data. The project grew from 10 to 440 evaluators in 31 markets. Over two years, Appen verified and corrected more than 750,000 business listings, making the search engine more reliable for users.
Research Case Studies and Success Stories with Appen platform
CASE STUDY Allen Institute for AI
Appen Platform helped the Allen Institute for AI improve Semantic Scholar's citation intent feature. AI2 needed large-scale, accurate annotation for research paper citations. Appen provided crowdsour...ced data labeling and quality control tools. The platform enabled fast task setup, real-time feedback, and easy customization. Citation intent classification now covers over 100 million citations with over 80% accuracy. Eight million scholars use Semantic Scholar monthly, benefiting from better research discovery.
Appen Platform helped the Allen Institute for AI improve Semantic Scholar's citation intent feature. AI2 needed large-scale, accurate data labeling for academic research papers. Appen provided crowds...ourced annotation and quality control tools. The platform enabled fast task setup, real-time feedback, and high accuracy. The citation intent model reached over 80% accuracy and now covers 100 million citations. Eight million scholars use Semantic Scholar each month.
Information Technology and Services Case Studies and Success Stories with Appen platform
CASE STUDY ReflexAI
ReflexAI used Appen's AI training data services to build HomeTeam, an AI-powered mental health support platform for veterans. ReflexAI needed high-quality, sensitive data to train their model for rea...listic and empathetic conversations. Appen provided expert support and diverse data contributors to fine-tune the AI. As a result, ReflexAI achieved 93% positive user feedback for HomeTeam. The platform now helps veterans and crisis counselors practice mental health conversations safely.
Appen’s AI Data Platform helped Onfido improve its AI fraud detection. Onfido needed secure, accurate data labeling for biometric and document checks. Appen provided a custom on-premise labeling tool... that met strict privacy and security needs. The solution handled images, videos, and documents, and supported large-scale data processing. Onfido saw a 10x improvement in AI fraud detection performance.
Appen’s AI Data Platform helped Onfido improve its AI fraud detection. Onfido needed secure, accurate data labeling for biometric and document checks. Appen delivered a custom on-premise labeling too...l, meeting strict privacy and flexibility needs. The solution enabled Onfido to process large data volumes and adapt to new fraud tactics. Onfido saw a 10x improvement in AI fraud detection performance.
Appen provided pre-labeled French lexicon datasets to MediaInterface for their expansion into France. MediaInterface needed high-quality French names and place names to support their speech recogniti...on product, SpeaKING, for healthcare documentation. Appen's datasets filled critical data gaps, helping MediaInterface build a comprehensive background lexicon. This enabled MediaInterface to offer better speech recognition for French healthcare clients. The solution improved product accuracy and allowed faster deployment in the new market.
Appen helped a leading AI platform improve its AI music generation feature. The client needed high-quality annotated music data to train their model. Appen provided expert music annotation and real-t...ime collaboration. This led to better quality AI-generated music, new advanced features, and a faster market launch. The solution improved user experience and engagement on the client's platform.
Appen used its AI data platform (ADAP) to help a leading model builder evaluate 3-6 large language models (LLMs) in complex domains like healthcare, legal, and finance. The project delivered over 500...,000 annotations in 5-day sprints, with 50,000+ annotations per sprint. Expert evaluators ranked model outputs for accuracy, relevance, and Responsible AI standards. The process gave the client insights to improve LLM performance and domain accuracy. The client also expanded into supervised fine-tuning and red teaming using these results.
Appen helped a leading AI platform improve its AI music generation feature. The client needed high-quality annotated music data to train its model. Appen recruited expert music professionals to class...ify and annotate music pieces. This led to better quality AI-generated compositions. The platform launched new advanced generative music features. Efficient annotation workflows enabled a faster market launch.
Appen Data Annotation Platform helped Brandwatch improve its digital intelligence insights. Brandwatch needed a faster, more reliable data annotation process. Appen's platform replaced their freelanc...e network, offering instant access to a global crowd and built-in QA tools. Brandwatch now ingests and analyzes over 500 million documents daily. The team can experiment quickly and ensure high-quality results. This made Brandwatch more agile and improved customer experience.
Appen helped a global technology company improve its large language model (LLM) using supervised fine-tuning. The project covered over 70 dialects in 30+ languages. Appen collected more than 250,000 ...rows of dialogue data with human feedback. Native speakers ranked model responses for accuracy, fluency, and cultural fit. The LLM now gives better, more accurate answers for users worldwide.
Appen provided Infobip with high-quality, multilingual datasets to train their conversational AI chatbots. Infobip needed fast, accurate data to improve chatbot performance and customer satisfaction.... Appen delivered hundreds of unique utterances per intent in several languages, meeting strict quality standards. This partnership helped Infobip speed up chatbot deployment and reduce the cost of customer service. Infobip valued Appen's managed services and quick response times for project support.
Appen Data Annotation Platform helped Trust Lab make social media safer. Trust Lab used Appen to collect and label user sentiment data on user generated content. This data let them identify unsafe co...ntent and improve trust and safety plans for their clients. Millions of judgements were made by contributors. Trust Lab now gives platforms better tools to protect users and keep them engaged.
FlamingoAI used Appen's training data to launch fully automated virtual assistants from day one. The challenge was to avoid the usual training phase and solve the cold-start problem for financial and... insurance clients. Appen provided high-quality, real-world data to pre-train the assistants. As a result, a US-based Fortune 100 client saw the assistant answer over 80% of customer questions and achieve a 30% conversion rate for life insurance quotes. FlamingoAI improved customer experience and sales conversion with this solution.
Appen’s AI data platform (ADAP) helped a leading model builder run rapid-sprint evaluations for 3-6 large language models. The project covered complex domains like healthcare, legal, finance, program...ming, math, and automotive. Appen delivered over 500,000 annotations in 5-day sprints of 50,000+ each. Expert evaluators benchmarked model accuracy, relevance, and Responsible AI compliance. The client used these insights to improve LLM performance and domain-specific accuracy.
Appen Data Annotation Platform helped Brandwatch improve digital intelligence insights. Brandwatch needed faster, more reliable data annotation to scale analytics. Appen replaced their freelance netw...ork with a global crowd and built-in QA tools. Brandwatch now analyzes over 500 million documents daily. The team experiments quickly and gets real-time feedback. Appen's platform increased agility and improved customer experience.
Appen's Data Annotation Platform helped CallMiner improve customer insights from call center data. CallMiner needed to analyze sentiment and emotion in large volumes of audio data but faced challenge...s with accuracy and scale. Appen provided a secure, flexible annotation solution that let CallMiner process tens of thousands of data samples, compared to just 3,000 before. The platform saved time, increased accuracy, and allowed CallMiner to expand its customer base and insights.
Microsoft Translator used Appen to expand its AI translation platform. Microsoft needed high-quality data for rare and under-resourced languages. Appen sourced language data from native speakers and ...provided expert annotation. Appen also addressed translation bias and handled complex phonetic systems. As a result, Microsoft Translator now supports 110 languages, with Appen contributing data for 108 of them.
Infobip used Appen's high-quality datasets to build conversational AI chatbots. Infobip needed fast, accurate, and diverse data to train their AI engine for global clients. Appen provided validated, ...multilingual datasets that met strict quality standards. This partnership helped Infobip reduce deployment time and improve chatbot performance, leading to better customer satisfaction and lower service costs.
Appen helped a global technology company improve its multilingual LLM using supervised fine-tuning. The project covered over 70 dialects in 30+ languages. Appen collected more than 250,000 rows of di...alogue data with human feedback. Native speakers ranked model responses for accuracy, fluency, and relevance. The LLM now delivers better cultural alignment and higher response quality for users worldwide.
Appen Data Annotation Platform helped Realeyes speed up video data labeling. Realeyes needed to process large amounts of video data fast and with cultural accuracy. Appen provided scalable annotation... tools and a diverse crowd of annotators. Realeyes reduced annotation time from three months to two weeks. They kept high data quality using Appen’s quality controls and custom integrations.
Appen Data Annotation Platform helped Realeyes speed up video data labeling. Realeyes needed to process large amounts of video data quickly and with cultural accuracy. Appen provided scalable annotat...ion, diverse human contributors, and strong quality controls. Realeyes reduced annotation time from three months to two weeks. The solution kept data secure and allowed custom tool integration for Realeyes.
HomeTeam by ReflexAI is an AI-powered platform for mental health support for veterans. ReflexAI partnered with Appen to get high-quality training data and fine-tune their model for sensitive conversa...tions. The solution focused on empathy, ethical AI, and realistic roleplay simulations. Appen's support helped ReflexAI achieve 93% positive user feedback. The platform now provides free, effective training for thousands of veterans and their supporters.
FlamingoAI used Appen's training data to launch virtual assistants that work without human training from day one. The challenge was to solve the cold-start problem for financial and insurance clients.... Appen provided high-quality, real-world data to pre-train the assistants. After seven weeks, the assistant answered over 80% of customer questions and converted life insurance quotes at a 30% rate. Customers found the experience quick and convenient.
Appen Data Annotation Platform helped Trust Lab make social media safer. Trust Lab used Appen to collect and label user sentiment data on user generated content. This data let Trust Lab see which con...tent was unsafe and provide metrics to internet platforms. Millions of judgements were made using Appen’s platform. The results helped platforms adjust trust and safety plans and remove harmful content.
Hospital & Health Care Case Studies and Success Stories with Appen platform
CASE STUDY MediaInterface
Appen provided pre-labeled French lexicon datasets to MediaInterface. MediaInterface needed this data to expand its speech recognition product, SpeaKING, into the French healthcare market. The datase...ts included about 21,000 French names and 14,000 place names. This helped MediaInterface fill critical data gaps and comply with data regulations. As a result, MediaInterface improved its product for French clients and expanded into a new market.
Other Industry Case Studies and Success Stories with Appen platform
CASE STUDY A leading global security and aerospace company
Appen’s platform helped a leading global security and aerospace company fight wildfires using computer vision. The company needed high-quality data labeling and model evaluation for complex EO/IR and... heat imagery. Appen provided computer vision automation, multi-sensor integration, and advanced annotation tools. The solution let the company annotate data at the storage point, saving time and improving data control. The platform enabled data scientists to use new data sources and improved model performance and annotation accuracy.
CASE STUDY A leading global security and aerospace company
Appen’s platform helped a leading global security and aerospace company fight wildfires using computer vision. The challenge was to train AI models with high-quality, complex data from multiple senso...rs. Appen provided automated data labeling and annotation tools, allowing the company to keep control of its data and save time. The solution enabled the use of new data sources and improved model performance. Project metrics tracked accuracy and boosted confidence in the results.
