Appen provides an AI data platform (ADAP) and managed services to collect, annotate, fine-tune, and evaluate multimodal datasets at scale using a large global contributor network.
Appen provides high-quality, scalable AI training data and human-annotated datasets used to build and improve AI and machine learning systems. The company offers an end-to-end approach that combines a software platform (ADAP) with flexible services to support data collection, data annotation, fine-tuning, and model evaluation across multiple modalities such as text, image, audio, video, and specialized formats. With more than 25 years of experience in data and AI, Appen positions itself as a long-standing provider of datasets and workflow expertise that support the full AI lifecycle. It emphasizes trustworthy, traceable processes with quality controls, human oversight, and tooling designed to accelerate iteration cycles and improve model performance. Appen operates a large crowd-based workforce model, stating a global network of over 1 million contributors / AI training specialists used for collection, labeling, and evaluation tasks. The company highlights multilingual and multi-locale capabilities, including support for hundreds of languages and broad geographic coverage for both customers and contributor sourcing. Beyond delivery scale, Appen highlights compliance and security posture for sensitive workflows, including SOC 2 Type II, GDPR alignment, HIPAA compliant solutions, and ISO/IEC 27001:2013 certification (via TÜV Rheinland). It also operates multiple offices and facilities across the United States, Australia, the United Kingdom, China, Japan, the Philippines, India, and Vietnam.
HC score
verified business cases
Custom and off-the-shelf data collection services including remote, on-site, device-based, and location/POI collections; supports image, video, speech/audio, text, documents, and location data, with preparation/annotation available via ADAP.
Remote collection
On-site sessions
Device collection
Custom data collection via remote, on-site, device-based, location/POI, and off-the-shelf dataset options, with workflow design and delivery through Appen’s platform and mobile app.
Remote collection
On-site collection
Device collection
Enterprise AI data platform that merges automation and human oversight to manage data preparation and model evaluation workflows (annotation, classification, preference scoring, A/B testing, user testing, red teaming, benchmarking) across multiple modalities.
Workflow customization
Multi-stage review
AI-assisted annotation
A flexible enterprise AI data platform that merges automation and human oversight to manage data preparation and model evaluation workflows across modalities (text, image, audio, video, 3D/4D), including annotation, classification, preference scoring, and evaluation methods like A/B testing, benchmarking, and red teaming.
Workflow customization
Multi-stage review
Contributor analytics
Human-expert powered sourcing, curation, annotation, and evaluation of high-quality training datasets across modalities (text, image, audio, video) including hard-to-find and niche data requirements.
Data sourcing
Data curation
Bias evaluation
Human-expert powered data sourcing, curation, annotation, and evaluation services to produce high-fidelity training datasets for deep learning and traditional AI applications across modalities and industries.
Custom collection
Human annotation
Bias review
Managed annotation services for text, audio, image, video, and multimodal datasets (e.g., sentiment, intent, NER, transcription, object detection, tracking, event detection) supported by Appen’s crowd and tooling.
Text annotation
Audio transcription
Image labeling
Services for LLM data creation and improvement including supervised fine tuning, human preference ranking (RLHF/DPO), evaluation & A/B testing, red teaming/model safety, and RAG data preparation.
Supervised fine tuning
Preference ranking
LLM evaluation
Services to support LLM development and enterprise customization, including supervised fine-tuning datasets, RLHF/DPO preference workflows, RAG data preparation, red teaming, and LLM evaluation and A/B testing.
Supervised fine tuning
Preference ranking
LLM evaluation
Licensable catalog of ready-to-use datasets across audio, image, video, text, and location data, described as spanning hundreds of datasets across many languages and countries.
Licensable datasets
Multimodal catalog
Immediate availability
Product Manager
We were expanding to a new market. Although we had a fully localized software, we were lacking resources, so our clients could not optimally use it. Appen helped us out with French lexicon data.
Vice President of AI
Appen is so fast. Using their platform, we could do overnight what used to take us a month. Appen is wonderfully efficient.
Director of the Data Science Institute
Appen’s platform is really easy to use. What makes it great is you can reach so many different channels because of its global outreach.
Gaming company specialist
Appen provided the integration of different tools and options in terms of using our internal labelers. Being able to monitor contributor performance and have a system that's already integrated with that capability was super helpful. Not to mention the scalability of it.
VP of Data Science
Compared to our freelancing network, Appen’s platform allows us to iterate quickly with our experimental design and data collection. This is an incredible benefit to us.
Vice President of Operations
Our integration with Appen's platform allowed us to scale up our annotation operations by collecting multiple types of high-quality annotations for thousands of hours of video footage, all in a short amount of time.
Assistant Professor of Biology
What would have taken one person 1,500+ hours over a year or more was done in a few weeks by contributors through the Appen platform (then Figure Eight).
In our mission to create world-class artificial intelligence chatbots at record speed, high-quality data sets are essential. Appen is a very important partner in this process because we can rely on them for exactly that: speedy and high-quality datasets that we use to train our AI engine
A top automotive OEM needed speech training data to power connected-car voice recognition across global markets. The customer faced the challenge of supporting drivers in many regions with consistent voice functionality. It needed multilingual coverage that could scale over time. A long-term training data development effort was implemented to support multilingual voice capabilities. The work focused on building speech training data suitable for connected-car voice recognition. The partnership sustained ongoing development to meet global market needs. The initiative supported voice recognition capabilities in more than 20 languages. It sustained delivery through a partnership that lasted over 10 years. This enabled the OEM to maintain multilingual voice recognition support across global markets.
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A leading multilingual search engine provider aimed to expand its international search quality operations. It needed to support multiple languages and regions while maintaining consistent quality standards. The scope required scaling processes across diverse markets. The customer implemented vendor-neutral quality analyst support along with quality management support. This approach enabled rapid scaling of the search quality program. The implementation was designed to operate consistently across multiple regions. The program expanded international search quality operations across 25 markets. The vendor-neutral analyst model supported fast rollout while maintaining operational coverage. The customer extended its search quality footprint to new regions within the defined scope.
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A major international software provider needed to update its Unicode Common Locale Data Repository (CLDR) with reliable local expertise. The customer faced the challenge of ensuring the updates reflected accurate local knowledge across many geographies. They required dependable in-market support to complete the work at scale. In-market resources were provided to support the Unicode CLDR update. The implementation focused on supplying local expertise in the relevant locations. Support was delivered across a wide set of geographies to enable the update effort. The engagement covered 66 markets. The customer received in-market resources to support its Unicode CLDR update across those markets. This provided localized support across many geographies for the update work.
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A leading software company needed to ensure an LLM image generator produced high-quality, culturally relevant designs for global audiences. The customer faced risk that outputs would not translate well across languages and locales. They also needed confidence that the generator’s designs met quality expectations in each market. Human evaluation and quality checks were implemented across languages and locales. The approach focused on reviewing image generator outputs for quality and cultural relevance. The program was executed across a broad set of markets to reflect real-world usage. The evaluation program covered 20+ languages. The customer gained visibility into how the image generator performed across different locales. Quality checks and human review helped validate that outputs aligned with expectations for global cultural relevance.
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A leading technology company needed to improve multilingual LLM performance at scale. The existing model required better quality across many language variations. The company faced the challenge of addressing multilingual performance consistently across dialects. The team implemented an approach that combined human preference rankings with supervised fine-tuning. Human evaluations were used to rank model outputs. Those rankings informed supervised fine-tuning to improve multilingual behavior. The work delivered improved multilingual LLM performance across 70 dialects. The results were applied at scale to cover a wide set of dialectal variation. The company received the improvements across the full 70-dialect scope.
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Johns Hopkins University
Johns Hopkins University needed to label and analyze behavioral neuroscience data. Completing the work would have taken a single person over a year. The scale and time required created a major manual workload. The team implemented a contributor-powered annotation approach using a data platform. This approach distributed the labeling and analysis work across contributors. It enabled the university to process the behavioral neuroscience dataset more quickly than a single-person effort. The project avoided 1,500+ hours of manual effort. The work was completed in only a few weeks instead of a year or more. This accelerated the labeling and analysis timeline substantially.
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Onfido
Onfido needed to improve its fraud detection performance. Existing approaches did not deliver the level of detection accuracy required. The company faced pressure to enhance performance while supporting its AI development needs. Onfido implemented custom on-premise AI data solutions. It also used tailored training data workflows to better support model development. These changes aligned data preparation and training processes with its fraud detection objectives. The implementation resulted in a 10x improvement in AI fraud detection. Fraud detection performance increased substantially versus the prior baseline. The outcome validated the effectiveness of the on-premise data solution and training data workflow approach.
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Full service creative production company helping brands maximise the impact of their marketing content
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Empowering US startups with unrivaled access to global engineering talent, seamless hiring, and improved retention.
Human Cloud Verification ensures that the listed end customer is verified. It's used across kudos, customers, and business cases, and performed by Human Cloud. Think about it like a background check.
An independent global marketing consultancy delivering outsized growth.
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