Scale

What is Scale AI

Scale AI is a data and model development platform that delivers high-quality training data, evaluations, and orchestration for AI systems across autonomy, mapping, AR/VR, robotics, automotive, and the public sector. Its Scale Data Engine powers dataset curation, labeling, and synthesis; supervised fine-tuning (SFT) and RLHF align models to task goals; and the Scale GenAI Platform supports full-stack generative AI workflows. With Scale Donovan for mission-critical agentic AI and rigorous model and application evaluation, Scale AI helps teams ship reliable, production-grade AI faster.

Scale AI Main Features

• Scale Data Engine: End-to-end data discovery, curation, annotation, and synthetic data generation, with programmatic pipelines for continuous training.
• High-quality labeling: Managed human-in-the-loop annotation with layered quality controls and auditable workflows for safety-critical domains.
• SFT and RLHF: Supervised fine-tuning and reinforcement learning from human feedback to align models with clear task rubrics and policies.
• GenAI Platform: Full-stack generative AI tooling for data prep, prompt and tool orchestration, evaluation, and safety checks.
• Donovan (Agentic AI): Mission-critical agent orchestration for operational workflows and decision support with traceability.
• Model and app evaluation: Benchmarking, red-teaming, and continuous evaluation to measure quality, bias, safety, and reliability.
• Industry-grade modalities: Support for text, images, video, geospatial data, sensor fusion, and automotive perception datasets.
• APIs and governance: Integration-friendly APIs plus governance, role-based access, and audit trails for enterprise control.

Who Should Use Scale AI

Scale AI fits enterprises and teams building production AI: autonomous driving and robotics groups, mapping and geospatial analytics teams, public sector programs, model developers seeking high-quality training data, and enterprise product owners who need reliable generative AI, evaluation, and governance across complex data modalities.

Scale AI How-To Steps

1. Define your objective, target metrics, modalities, and safety requirements.
2. Connect data sources securely and import text, imagery, video, logs, or telemetry.
3. Configure the Data Engine: ontology/labels, quality thresholds, and review policies.
4. Select pipelines: annotation, synthetic data generation, SFT, and RLHF as needed.
5. Set up evaluation suites and safety tests to establish a reliable baseline.
6. Train or fine-tune models and iterate based on evaluation signals.
7. For agentic workflows, configure Donovan agents, tools, and playbooks.
8. Deploy with monitoring, then expand datasets via active learning and continuous evaluation.

Scale AI Industry Use Cases

Autonomous driving teams curate multimodal perception and planning data, annotate long-tail edge cases, and evaluate model regressions. Mapping providers extract roads, lanes, and POIs from aerial and ground imagery. Public sector programs deploy evaluated GenAI assistants for analysis and triage. Robotics teams refine perception and control with curated simulation-to-real datasets. Enterprises validate RAG applications with continuous evaluation for accuracy and safety.

Scale AI Pricing Model

Scale AI typically offers custom, enterprise contracts with pricing that varies by product (Data Engine, GenAI Platform, evaluation, Donovan), data volume, modality, and service level. Usage-based components and solution bundles are common. For specifics, available pilots, or deployment options, contact Scale AI’s sales team.

Scale AI Pros and Cons

Scale AI FAQs

• What is the difference between the Scale Data Engine and the GenAI Platform?

  The Data Engine focuses on data operations—curation, labeling, and synthesis—plus SFT/RLHF pipelines. The GenAI Platform centers on building and evaluating generative applications, including prompt/tool orchestration and safety testing.

• Does Scale AI support human-in-the-loop workflows?

  Yes. Scale AI combines automated pipelines with managed human review for annotation, preference data, and evaluation, improving quality and auditability.

• Which data types are supported?

  Scale AI supports text, images, video, geospatial data, and multimodal sensor inputs commonly used in automotive, mapping, AR/VR, and robotics.

• Can I bring my own models?

  Scale AI is designed to work with your models or third-party providers, enabling fine-tuning, evaluation, and application integration through APIs.

• How does RLHF work on the platform?

  Teams collect preference data with clear rubrics, train reward models, and optimize policies to align behavior, with evaluation loops ensuring safety and performance.

• Is there a free trial?

  Availability varies by product and engagement. Contact sales to discuss pilots, proofs of concept, and deployment options.