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Generative AI

Enterprises like yours are using Uber's Scaled Solutions to build, annotate, curate, test, and localize their generative AI offerings.

Scaled Solutions

8+ years

Expertise in managing large-scale AI and ML operations

Localization

100+ languages

Including languages in Asia, Europe, Latin America, the Middle East, and more

Data annotation

25+ capabilities

Chat or text summarization

Consensus labeling

Data collection: audio/video/image

Open-ended text descriptions to image/video/anime

Preference rating for multi-responses

Prompt response evaluation/ranking

Side-by-side review and rating/edits

Synthetic data creation

Generative AI

10+ areas of expertise

Auto

Entertainment

Finance

Gaming

Language

Programming

Reasoning

Science

Sports

TV and movies

Product testing

Bespoke AI and ML frameworks for your product

  • Define use cases and behavior
  • Ensure readiness by collateral development
  • Validate coverage of test cases
  • Evaluate model learning ability
  • Evaluate speed to response, error rate, and time to load responses
  • Monitor memory, network usage, and configuration
  • Develop A/B testing to validate fluency, contextual awareness, and relevance
  • Test linearity of decision for response coherence
  • Validate accessibility, UI/UX, user engagement, linguistic accuracy, and many more
  • Benchmark against other AI/ML products

Use cases

Synthetic data creation

Creating Q&A pairs from scratch across a broad range of topics (such as travel and food) or for specific specialized categories (like programming and finance) and in 100+ languages globally.

Open-ended text descriptions to image/video/anime

Providing text summaries based on visual aids for gen AI start-ups creating image/video/anime from text prompts or vice versa.

Data collection: audio/video/image

Different activities, different voices, different acoustic conditions, different regions and genders, and more.

Preference rating for multi-responses

Rating/ranking preference of multiple responses for the same prompt (LLMs or text to image/video models)

Consensus labeling

Classification or rating done across multiple diverse groups (such as regions and genders) to get a consensus score and eliminate bias.

Chat or text summarization

Providing a summary and/or evaluating model output on summarization.

Side-by-side review and rating/edits

Side-by-side review of multiple model responses to a prompt, followed by rating or editing the responses.

How is Uber different?

How is Uber different?

Uber

Others

Subject matter expertise

Uber’s team of tech program managers has decades of expertise leading globally scaled operations across our core verticals and apps, including rides, delivery, freight, and AI applications. We use this extensive experience to design solutions and work with our network to make sure your needs are met with precision and efficiency.

Only provide expertise for managing operations.

Product quality

Our AI/ML product testing framework conducts ongoing evaluations of your product(s) to assess model performance, usability, and functionality. Insights gained from these tests directly inform customer requirements, driving continuous enhancements and ensuring that your product not only meets but also exceeds expectations.

N/A

Process quality

We emphasize a dynamic, iterative process designed to integrate feedback from domain experts, evaluators, and seasoned SMEs in our network of operators directly into the guidelines, ensuring continuous improvement and relevance.

Customer provided guidelines to deliver datasets.

Additional investment

We offer the expertise of SMEs to craft comprehensive style guides, encapsulating cultural nuances, linguistic authenticity, and emotional intelligence. Additionally, we provide a skilled partner engineering team to develop technological solutions that support human QA, such as plagiarism detection tools. Our eLearning platform delivers training for globally dispersed operators, ensuring consistent and up-to-date knowledge dissemination. We’re committed to defining metrics for process and product evaluation, identifying trends and patterns, and using in-depth metric analysis to inform future roadmaps.

Provide only training, policy, and operations data analysis.