Free Resource · AI Teams

12 questions to ask your AI annotation vendor
before you sign.

Most AI teams hire annotation vendors after a demo and a pricing call. By the time quality problems show up, you've already lost weeks of model training time. This checklist helps you hire right the first time.

  • The one question that instantly reveals if a vendor has domain expertise in your field
  • How to evaluate QA processes — and why "self-reviewed" is a red flag
  • What to ask about data security for proprietary training data
  • How to align delivery cadence to your model training schedule
  • The 3 contract terms annotation vendors hope you don't ask about

Download the Free Checklist

1-page PDF · Instant download

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What's Inside

Four sections. 12 questions. No fluff.

Section 1

Domain Expertise

  • Proof of domain-specific annotation experience
  • How annotators are trained on new taxonomies
  • Edge case and ambiguity escalation process
Section 2

Quality & Accuracy

  • Inter-annotator agreement (IAA) benchmarks
  • Separation of annotation and QA layers
  • Accuracy SLAs and remediation terms
Section 3

Speed & Scale

  • Time from contract to first delivery
  • Surge and scale-down flexibility
  • Sprint cycle alignment to model training
Section 4

Integration & Operations

  • Tool and pipeline compatibility
  • Dedicated vs. shared project management
  • Data security and confidentiality terms
"We needed annotators who could understand our spatial data from day one, not spend weeks learning what a planogram is. Trinovation was the only vendor who showed up already knowing the domain."
— Head of AI, Series A Spatial Computing Company · $42M raised
2 wks
Time to First Delivery
30%
Model Accuracy Improvement
550+
Stores Supported