A leading professional social network needed qualified expertise to evaluate and rank AI co-pilot conversations. It faced difficulty ensuring assessments reflected relevant domain knowledge. It also needed a consistent way to prioritize AI co-pilot outputs based on structured review. Relevant domain expert evaluators were sourced to perform conversation assessment and ranking. The team implemented a structured evaluation approach for reviewing AI co-pilot conversations. This ensured rankings were grounded in appropriate expertise and applied consistently across outputs. The customer received evaluated and ranked AI co-pilot conversations. The structured process enabled clearer evaluation and prioritization of AI co-pilot outputs. This improved the customer’s ability to review and select higher-quality AI co-pilot conversation results.

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A top cloud computing company needed to improve an LLM-powered business intelligence platform. The existing model performance required enhancement to better support the platform’s business intelligence use cases. They sought a way to augment the model without disrupting the broader product experience. The team implemented multimodal fine-tuning support to augment and improve the underlying model. This work focused on strengthening the platform’s GenAI capabilities by refining how the model handled multimodal inputs. The implementation targeted overall model quality for the LLM-powered BI platform. The company delivered an enhanced GenAI business intelligence platform. The model performance improved as a result of the multimodal fine-tuning support. No specific performance figures were provided.

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A global healthcare technology company needed more reliable outputs from a vision-language transformer that generated clinical reports from radiologic images. The model’s report generation had to be clinically accurate to support radiology workflows. The customer sought an approach to improve reliability without changing the underlying use case. The team implemented supervised fine-tuning (SFT) as part of an evaluation and improvement effort. They used medical data that was expert-reviewed and scored to guide the fine-tuning process. This setup focused on aligning the model’s outputs with clinically accurate report generation requirements. After supervised fine-tuning, the reliability of the model’s outputs improved for clinical report generation. The vision-language transformer produced more dependable radiology reports from radiologic images. The improved reliability supported clinically accurate report generation for the customer’s needs.

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A math reasoning dataset was needed to support generative AI training with detailed step-wise explanations and corrections. The customer lacked an original corpus of chain-of-thought problems suitable for training. They required coverage across a wide range of formal and applied mathematics topics. An original corpus of chain-of-thought math problems was produced to meet the dataset requirements. The problems included detailed step-wise explanations to support reasoning training. Step-wise corrections and annotation were included to improve clarity and usability. The effort delivered a 1,000-problem dataset designed for generative AI training. The dataset spanned 60+ formal and applied mathematics subdomains. It included step-wise corrections as part of the completed corpus.

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A leading autonomous vehicle company needed to scale HD mapping output while avoiding increases in headcount. Existing workflows could not support higher throughput without adding more people. The company required a way to expand delivery capacity without expanding the internal team. The company implemented process automation to reduce manual effort in HD mapping production. It introduced tool innovation to streamline workflows and improve throughput. It also used expert data annotation to support consistent mapping output at scale. The approach increased HD mapping throughput while keeping staffing levels unchanged. HD mapping delivery scaled without adding team members. The company met scaling needs without expanding headcount.

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The customer needed vehicle damage detection that accurately identified damage in real time. The system had to perform reliably at scale while still handling difficult edge cases. Accuracy requirements were high, and mistakes on rare scenarios could degrade overall performance. Expert annotation was implemented to improve labeling quality and consistency. Edge case handling was incorporated to address rare and difficult damage scenarios. Adaptive workflows were used to support robust model performance as needs evolved. The implementation enabled real-time AI vehicle damage inspection at scale. Model performance improved through better handling of edge cases. The customer achieved high precision in production while maintaining scalable operations.

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A leading automotive technology company needed faster delivery of high-quality 3D sensor fusion annotations. Existing processes could not deliver the required throughput without risking quality. The customer faced pressure to accelerate completion of 3D sensor fusion labeling work while maintaining accuracy. Smart tools and automation were implemented to streamline the 3D sensor fusion annotation workflow. The approach optimized production steps to improve throughput while preserving quality controls. The updated process supported faster delivery of annotated data. The customer achieved accelerated completion of 3D sensor fusion labeling work at high quality. Throughput improved as the workflow became more streamlined. High-quality 3D sensor fusion annotations were delivered faster than before.

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An autonomous trucking initiative required precise lane marking data to improve safety at scale. The customer needed consistent, high-quality lane marking annotations suitable for autonomous truck development. Existing outputs were not sufficient to support safety-focused development across large volumes. Expert annotators were applied to produce lane marking annotations. Custom tooling was implemented to support consistent, high-precision lane marking work. The approach was designed to scale lane marking annotation output while maintaining quality. The project delivered consistent, high-precision lane marking annotations suitable for autonomous truck development. The output supported safety-focused use cases while scaling to meet program needs. The customer received lane marking data produced with expert annotation and custom tooling.

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A global pharmaceutical company needed scalable, clinical-grade histopathology annotations. The customer also needed to keep costs aligned with U.S. healthcare AI development realities. Existing approaches made it difficult to balance quality, scale, and cost constraints. A dual-shore pathology workflow was implemented. India-based pathologists performed first-pass tissue slide annotation. U.S.-based subspecialists conducted review to ensure clinical-grade output. This structure supported scaling while maintaining the required clinical rigor. The engagement delivered clinical-grade labels at scale. The cost structure remained aligned with U.S. market constraints. The customer achieved a scalable annotation pipeline suitable for healthcare AI development.

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