Scale AI, Inc. provides a full-stack platform and services for data, post-training (e.g., RLHF), evaluations, and agentic infrastructure to help AI labs, enterprises, and governments build and deploy reliable AI systems and AI agents.
Scale AI, Inc. builds technology and services to develop reliable AI systems for important decisions. The company provides high-quality data and full-stack technologies that power leading AI models and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. Scale offers a suite spanning data collection/curation/annotation, generative AI post-training (including RLHF), model evaluation, safety and alignment work via its SEAL (Safety, Evaluations, and Alignment Lab) initiative, and agentic infrastructure to deploy and operate AI agents. Its offerings are positioned from “data to deployment,” supporting both frontier model builders and applied enterprise and public-sector use cases. Scale serves AI labs, governments (including U.S. public sector organizations), and Fortune 500 enterprises, emphasizing production-grade reliability, security, and evaluation rigor. The company highlights a large volume of human decisions used to train models and significant contributor payouts, and it provides certified compliance for its cloud platform. Scale also publishes research, benchmarks, and leaderboards for LLM evaluations, and offers forward-deployed teams and services (e.g., enterprise agentic solutions, red teaming) to accelerate AI transformation and ensure safe, reliable deployment.
HC score
verified business cases
Combined expert services and platform support to build, translate data for, train (post-training), red team, evaluate, and scale domain-specific enterprise AI agents.
Forward deployed teams
Agent training
Data translation
A public-sector product for deploying specialized AI agents for mission-critical workflows, including a no-code agent factory, testing/evaluation, and an agent arsenal aligned with DoD AI ethics principles and engineered for accountability and scale.
No-code agents
Test & evaluate
Agent guardrails
Public sector product to customize, evaluate, and deploy mission-tailored AI agents for mission-critical workflows, integrating with SGP and aligned to DoD AI ethics principles.
No-code agent factory
Test & evaluate
Agent arsenal
Platform to collect, curate, and annotate data; train models and evaluate in iterative loops. Supports multiple annotation types (text, image, video, 3D) and workflows including data generation, RLHF, red teaming, and evaluation.
Data annotation
Data curation
Data collection
Enterprise agentic infrastructure to build, evaluate, train, deploy, and continuously improve AI agents and applications that reason over enterprise data and take action with tools.
Agent execution
Agent operations
Observability
Expert-driven private evaluations and leaderboards benchmarking frontier, agentic, safety, and tool-use capabilities of LLMs using robust datasets and precise criteria.
Private evaluations
Benchmark leaderboards
Robust datasets
Entrepreneur and Investor, and Former CEO of GitHub
We’re going to need a lot more investment in high-quality evals and benchmarks to help us understand the actual comparative utility of the various models. This new set of private evals and leaderboards from Scale are great to see
Founder
Nice, a serious contender to LMSYS in evaluating LLMs has entered the chat: SEAL Leaderboards. LLM evals are improving, but not so long ago their state was very bleak, with qualitative experience very often disagreeing with quantitative rankings. Good evals are very difficult to build…They have to be comprehensive, representative, of high quality, and measure gradient signal, and there are a lot of details to think through and get right before your qualitative and quantitative assessments line up. …Good evals are unintuitively difficult, highly work-intensive, but quite important, so I'm happy to see more organizations join the effort to do it well.
CEO
Great to see Gemini 1.5 pro top the new Scale SEAL leaderboard for adversarial robustness! Congrats to the entire Gemini team…and the AI safety team for leading the charge on building in robustness to our models as a core capability. Thanks to the Scale AI team for doing the vital work to create these rigorous benchmarks, the field needs more great work on topics like this
Founder and CEO
We partnered with Scale AI to work with Enterprises to adopt Llama and train custom models with their own data. We are excited to collectively make Llama the industry standard and bring the benefits of AI to everyone.
Square
Square needed a more efficient way to gather annotations while maintaining quality. The team also wanted to enforce best practices throughout the annotation workflow. An engineer sought a way to improve the process without sacrificing annotation standards. Square implemented a workflow that used the UI to manage annotation tasks. The engineer used a built-in worker evaluation pipeline to monitor and enforce quality. The team also used batch options to streamline how annotation work was organized and executed. Square saved time by relying on the UI, the worker evaluation pipeline, and batch options. The approach helped enforce best practices across the annotation process. Square also cited a good price point for annotations, though no quantified cost results were provided.
Skills
Project Details
advanced.farm
advanced.farm needed high-quality training data and greater operational scale to support apple-picking. The team faced a challenge in building a reliable pipeline that could produce consistent labels for model training. The existing approach did not meet the quality and scalability needs required for ongoing operations. To address the gap, advanced.farm implemented in-house labeling operations. The customer used Rapid to support the labeling workflow and enable scaling of the apple-picking program. This approach focused on creating a controllable, internal process for producing training data. As a result, advanced.farm scaled apple-picking operations using its in-house labeling setup. The new workflow supported the creation of high-quality training data for apple-picking. No quantitative results were provided in the original case excerpt.
Skills
Project Details
Orchard Robotics
Orchard Robotics pursued improved precision crop management for its operations. The customer needed a way to enable state-of-the-art approaches, but the excerpt did not specify measurable impact metrics. The available information focused on the objective rather than quantified outcomes. To address the need, Orchard Robotics implemented Rapid to enable state-of-the-art precision crop management. The excerpt indicated that this implementation supported the customer’s effort to improve how it managed crops with greater precision. No additional deployment details or technical components were provided. As a result, Orchard Robotics enabled state-of-the-art precision crop management using Rapid. The excerpt did not report quantified outcomes, operational improvements, or ROI metrics. The documented result remained limited to the stated enablement rather than measured performance changes.
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Project Details
Nuro
Nuro needed to uncover rare classes within unlabeled datasets. The available excerpt described this as a challenge of finding rare classes hidden in data that had not been labeled. This made it difficult to identify and surface the needed examples from the broader dataset. Nuro used Nucleus Object Autotag to mine for rare classes. The implementation focused on applying object autotagging to unlabeled datasets to discover and extract rare classes. This approach allowed the team to search for rare classes without first fully labeling the data. The effort resulted in rare classes being mined from unlabeled data using object autotagging. The excerpt did not include any numerical outcomes, performance improvements, or time savings. As a result, no quantified results were reported beyond the described use case.
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Project Details
Velodyne
Velodyne needed to identify edge cases in 3D data and prioritize the most valuable data for annotation. The existing workflow made it difficult to surface rare or challenging scenarios efficiently. This limited the team’s ability to focus annotation efforts on the highest-value data. Velodyne used Nucleus to find edge cases in its 3D data. The implementation supported curation of high-value data for annotation. This approach helped the team organize and prioritize what to send through the annotation pipeline. Velodyne identified edge cases in 3D data using Nucleus. The team curated and prioritized high-value data for annotation based on those findings. The provided excerpt did not include quantified outcomes beyond these reported capabilities.
Skills
Project Details
A bill pay use case was powered by Document AI. The excerpt did not name the customer associated with the implementation. No numeric performance or business impact metrics were provided. An automated bill pay workflow was implemented using Document AI. The implementation focused on powering bill pay through document processing. The excerpt did not include additional details about the system architecture or rollout. The implementation delivered an automated bill pay use case powered by Document AI. The excerpt did not report any quantitative outcomes, performance measures, or business impact. No customer-specific results were provided.
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Cohere
Cohere aimed to improve the quality of its large language models. The case excerpt stated that the company sought to enhance its LLM performance. No quantitative results were provided in the excerpt. Cohere implemented a platform and related products referenced in the excerpt to support its LLM enhancement efforts. The excerpt indicated these tools were used to improve model quality. The excerpt did not provide details about specific workflows, timelines, or measurement approaches. As a result, the excerpt stated that Cohere enhanced its large language models using the referenced platform and products. However, the excerpt did not include quantified outcomes, benchmarks, or performance metrics. No numerical improvements or business impact figures were reported.
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TIME
TIME sought to innovate in the media industry using AI. The organization wanted to explore new ways to apply AI to its media content and workflows. The excerpt did not provide specific measurable outcomes or performance targets. TIME implemented a TIME AI initiative built on an AI platform. The initiative focused on using AI to support innovation in the media industry. The excerpt did not describe the specific technical architecture or the exact scope of features deployed. TIME delivered the TIME AI initiative as a step toward AI-driven innovation. The excerpt did not report quantified business impact, such as revenue growth, cost savings, or productivity gains. As a result, no measurable outcomes could be attributed from the provided information.
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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.
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.
