The Evaluation Landscape

As AI systems move from demos to production, evaluation becomes critical. But the market is fragmented:

DIY Internal Evaluation

  • Your engineers rate their own outputs
  • No independent validation
  • Limited bandwidth as team scales
  • Expertise gaps in security and bias

Automated Testing Platforms

  • Fast and scalable, but miss edge cases
  • LLM-as-judge inherits model biases
  • Rules-based testing is brittle
  • Can't evaluate subjective quality

General-Purpose Data Labeling

  • Commodity crowdworkers lack expertise
  • Quality inconsistent across evaluators
  • No domain knowledge or security training
  • Built for speed, not precision

SliceLabel

Specialized enough to catch what matters, fast enough to keep pace with development, rigorous enough to satisfy compliance and risk teams.

Built for AI Companies, Not Generic Labeling

01

Specialized Expertise, Not Commodity Labor

Them:

General crowdworkers with minimal training

Us:

Security researchers, domain experts, and AI safety specialists

Our evaluators include security researchers with adversarial ML experience, domain experts (physicians, attorneys, engineers), AI safety researchers familiar with alignment challenges, and former model developers who understand failure modes.

Why it matters: Edge cases, security vulnerabilities, and subtle failures require expertise to identify.
02

Built for Iterative Development

Them:

4-week onboarding, minimum contracts, rigid processes

Us:

72-hour pilot turnaround, flexible engagement models

We're built for AI development speed: pilot evaluations in 72 hours, weekly delivery cadences, no minimum commitments for exploratory projects, flexible team scaling.

Why it matters: AI development moves fast. By the time traditional providers ramp up, your system has already changed.
03

Quality That Withstands Scrutiny

Them:

Labels with minimal documentation or methodology

Us:

Evaluation reports built for regulatory review and board presentations

Every evaluation includes inter-rater reliability metrics, methodology documentation, edge case taxonomies, disagreement analysis and resolution, and clear audit trail.

Why it matters: When regulators ask "how do you know this is safe?" you need defensible answers, not just labeled data.
04

Deep Evaluation Design

Them:

"Send us your rubric and data"

Us:

"Let's design evaluation that catches what matters"

We partner on evaluation design: What are your actual failure modes? What edge cases should we prioritize? What metrics align with your risk profile?

Why it matters: Bad evaluation design catches the wrong things. You need partners who understand AI systems.
05

Security & Confidentiality as Default

Them:

Consumer-grade security, potential offshore data handling

Us:

SOC 2 Type II on roadmap, US-based teams for sensitive work

We handle proprietary models and training data, confidential business logic, PII and sensitive user data, pre-launch products and competitive intelligence.

Why it matters: Your AI system is competitive advantage. You need evaluators who treat confidentiality seriously.

What You Can Expect

01

Speed Without Sacrificing Quality

72-hour pilot turnarounds. Weekly delivery cadences. Rapid iteration aligned with your development speed.

02

Expertise When It Matters

Security researchers for red teaming. Domain experts for specialized validation. AI safety specialists for capability evaluation.

03

Quality That Withstands Scrutiny

Inter-rater reliability metrics. Methodology documentation. Audit trails. Built for regulatory review.

04

Partnership, Not Just Execution

We don't just execute your rubric. We partner on evaluation design and proactively flag edge cases.

05

Confidentiality as Default

Your AI systems are competitive advantage. We treat confidentiality seriously—NDAs, security requirements, US-based teams.

06

Continuous Improvement

We learn from every engagement. Feedback loops, methodology refinements, and evolving best practices benefit all our clients.

Ready to See the Difference?

Most clients start with a pilot evaluation to assess quality and fit. Typical pilot: 100-500 evaluations delivered in 72 hours.

Contact us: hello@slicelabel.com