Scale AI, Inc.
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.
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Solution Highlights
Products
Agentic Solutions for Enterprise
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
Donovan
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
Donovan
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
Scale Data Engine
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
Scale GenAI Platform (SGP)
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
SEAL Leaderboards (LLM Leaderboards)
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
Performance
Nat Friedman
Nat Friedman • 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
Andrej Karpathy
Andrej Karpathy • 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.
Demis Hassabis
Demis Hassabis • 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
Mark Zuckerberg
Mark Zuckerberg • 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.
Saved 0 Time via Worker Evaluation Pipeline and Batch Options
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.
Qualifications
Customers
Badges
Performance across Human Cloud, as measured by company interest, kudos, and business case success.
Features
About Scale AI, Inc.
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.
Additional Details
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