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 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 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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