Scale AI provides services and software to build and evaluate AI systems, including tailored training and feedback datasets, LLM capability and safety evaluations, and a platform to develop, test, and deploy generative AI applications. It also offers Donovan for public-sector agent workflows.
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TL;DR
Scale AI helps organizations build and run AI systems by providing
(1) training/evaluation data services
(2) tooling to evaluate model capability and safety
(3) platforms to develop and deploy generative AI applications and agent workflows, plus a public-sector offering (Donovan) for mission-focused deployments.
List your use case; map it to Data, Evaluation, or Platform.
What Scale AI Does in Plain Terms
Scale AI is a vendor you use when you need higher-quality data, repeatable model evaluation, and/or an enterprise platform to build and deploy generative AI apps and agents, especially when you want structured human feedback (e.g., RLHF-style preference data) and governance-oriented testing.
What You Actually Buy From Scale AI
GenAI Data Engine
What it is: A data service + delivery layer for generating tailored datasets curated by subject matter experts, including model evaluations and RLHF data, accessible via API, SDK, or a web frontend.
Typical outputs you can request:
- Custom data annotations (task-specific schemas/rubrics)
- Preference / ranking data used in RLHF-style tuning
- Evaluation datasets and human-graded outputs aligned to your criteria
When it fits best: You already have a model (or vendor model) and need better signals (training data, preference data, eval sets) to improve quality and reduce failures.
Scale Evaluation
What it is: An evaluation offering positioned around trusted evaluation for LLM capabilities and safety, including structured performance breakdowns and risk-oriented testing language (e.g., identifying vulnerabilities across categories).
What to validate in a demo:
- How evaluation sets are constructed and how overfitting is mitigated
- Rater QA / consistency controls
- How results are versioned and compared across runs/models
GenAI Platform
What it is: A platform positioned to develop, test, and deploy generative AI applications using proprietary enterprise data, with API, SDK, and web frontend access.
When it fits best: You’re building production GenAI apps/agents (often RAG + workflows) and want a vendor platform that standardizes development, testing, and deployment.
Donovan
What it is: A public-sector product framed around deploying specialized AI agents for mission-critical workflows and agent customization/evaluation/deployment.
Compliance note: Scale states specific supported environments include FedRAMP High Authorized (scope/boundary must be validated in procurement).
How Teams Typically Use Scale AI
Common patterns (pick the one that matches your buying motion):
- Improve model quality: commission better training signals (annotations + preference data) and run targeted evals.
- Prove readiness: establish capability/safety baselines and compare model versions on standardized reporting.
- Ship an enterprise GenAI app: implement a platform workflow for building/testing/deploying apps using proprietary data.
- Public-sector mission workflows: deploy agents where architecture/security constraints matter.
How to Evaluate Scale AI for Your Use Case
- Choose the primary job-to-be-done.
Are you buying data, evaluation, app platform, or public-sector deployment? - Demand an input→output definition.
For your pilot, specify:
- Inputs (data types, rubrics, policies, “what good looks like”)
- Outputs (datasets, eval reports, deployable workflow)
…and what “done” means.
- Define quality metrics before buying.
Require:
- Offline eval plan (datasets, scoring rubrics, pass/fail thresholds)
- Online monitoring plan (failure modes, escalation, review loop)
- Validate human expertise requirements.
Confirm who rates/labels, how they’re trained, and what QA controls exist. - Run a time-boxed pilot with a single representative use case.
Compare:
- Baseline vs post-Scale metrics
- Time-to-iteration and cost per improvement cycle
- Security/compliance: scope it precisely.
Ask for the exact environment boundary and evidence package for any authorization claims (e.g., FedRAMP High) rather than accepting broad statements.
Common Mistakes and Edge Cases
- Mistake: treating “data” and “evaluation” as the same purchase.
They can be linked, but procurement and success criteria differ: data improves training signals; evaluation proves capability/safety. - Mistake: skipping a written evaluation rubric.
Without a rubric, you’ll “feel” improvement but won’t be able to defend it. - Edge case: you only need a basic vendor overview.
If you aren’t running pilots or building production workflows, a lighter-weight internal brief may be enough.
How to Do This with CustomGPT.ai
If your immediate goal is to answer “What does Scale AI do?” consistently for stakeholders, create a cited internal vendor-brief agent in CustomGPT.ai that only references approved sources.
- Create the agent from vetted web sources (your approved Scale pages).
- Restrict and maintain the knowledge base (add/remove sources as docs change).
- Turn on citations so every key claim is traceable.
- Apply safety settings to reduce prompt injection and hallucinations.
- Deploy internally via link/embed/widget.
- Prevent unauthorized reuse of the embed code.
Conclusion
Scale AI supports building and evaluating AI systems through tailored training/feedback datasets, LLM capability and safety evaluations, and a GenAI development/deployment platform, plus Donovan for public-sector workflows.
Next step: Use CustomGPT.ai to deliver cited briefs via a 7-day free trial.
FAQ
Is Scale AI Only a Data Labeling Company?
Scale is widely associated with data labeling, but its current positioning spans data generation/feedback, model evaluation (capability and safety), and platform tooling for building and deploying GenAI applications, plus public-sector agent workflows. The right framing is “data + evaluation + deployment tooling,” not only labeling.
What’s the Difference Between GenAI Data Engine and GenAI Platform?
GenAI Data Engine is about producing and delivering datasets and feedback signals (including evaluation/RLHF data) via API/SDK/web. GenAI Platform is about developing, testing, and deploying GenAI applications using proprietary enterprise data, also via API/SDK/web. One is “signals/data,” the other is “application workflow and deployment.”
Does Scale AI Offer LLM Safety Evaluation or Red Teaming?
Scale positions its Evaluation offering around evaluating LLM capability and safety and describes identifying vulnerabilities across multiple risk categories. If safety evaluation is a key requirement, validate the exact evaluation sets, rater QA, and reporting/versioning you’ll receive in your pilot scope.
Can I Use CustomGPT.ai to Build an Internal, Cited Vendor Brief on Scale AI?
Yes. Create an agent using only the Scale pages you approve, enable citations, and keep “generate responses from” controls and security protections enabled so answers remain grounded. Start with the website connector and then manage sources as Scale updates docs.
How Do I Stop a CustomGPT.ai Vendor-Brief Agent from Drifting into Unapproved Sources?
Use a curated source set, keep citations on, and enable the platform’s recommended protections against hallucinations/prompt tampering. If you embed the agent, add a domain whitelist so others can’t reuse your embed code elsewhere. This keeps the agent’s output tied to the pages your team reviewed.