SafetyKit GTM brain

category

market profile

• text: Platforms must identify prohibited listings and harmful content across long-tail products and changing abuse patterns.
• text: Trust and safety operations need both specialized training data and policy-specific decisions at scale.

personas

• title: Head of Trust and Safety
  pains:

  • Counterfeit and recalled listings evade generic classifiers.
  • Policy decisions vary between reviewers as volume grows.
• title: Risk or merchant underwriting leader
  pains:

  • Harmful merchant sites and transaction laundering require investigation across content, ownership and payments.
  • New scam patterns make static controls stale.
• title: Trust and safety machine learning lead
  pains:

  • Scarce expert-labeled examples of niche goods limit model recall.
  • Training sets need continual refresh as behavior changes.

icp

• text: Large marketplaces with extensive product listings, including regulated goods and counterfeiting exposure.
• text: Payments companies and platforms with merchant website risk or significant user-generated content.

message pillars

• text: Specialist labels improve the data foundation for difficult enforcement decisions.
• text: Coverage extends beyond common examples into niche products and emerging scams.
• text: Buyers can use an API or train their own models.

objections

• objection: Our policy is too specific for generic moderation.
  response: Evaluate a sample against your policy line and specialist labels before deployment.
• objection: We already have an internal model team.
  response: Use the labeled datasets to train internal models rather than replacing the existing stack.
• objection: Abuse patterns change too quickly.
  response: Assess refresh cadence and recall on recent examples in a pilot.

competitors

• name: Pasabi
  why: Offers trust and safety fraud detection.
• name: Intrinsic
  why: Provides AI for trust and safety workflows.

keywords

• trust and safety AI
• marketplace counterfeit detection
• product recall detection
• content moderation data
• transaction laundering detection
• trust and safety labeled data

voice

• Direct and technical
• Confident about scale
• Focused on concrete abuse cases

signals

• text: Marketplace expanding product categories or facing counterfeit and recall enforcement.
• text: Payments platform investigating merchant websites or transaction laundering.

stack

• API

pricing

motion

channels

• SEO
• events
• LinkedIn

confidence

Founders

• name: David Graunke
  title: Founder
  role: Founder
  bio: David led engineering for risk reviews at Stripe for fraud, credit, content moderation, and financial crimes. He built the policy and workflow engine that scaled Stripe from internal reviewers to thousands of outsourced vendor agents.
• name: Steven Guichard
  title: Founder
  role: Founder
  bio: Steven is a cofounder of SafetyKit, working to replace human risk reviewers with language models. Previously he worked at Carbic as the first software engineer and later as the CEO. There he helped build ultrasonic flow sensors which were installed on oil pipelines and offshore rigs around the world. Steven also cofounded Thomas Street, a software design and engineering consultancy, where he worked with companies like Cisco, Roche, and DirecTV.