Kindred

Trust Center

Trust in a Kindred analysis means two things at once. Your data must be protected, and your thinking must never be manipulated. A standard product only has to earn the first. Kindred, because of what it does, has to earn both. The three pillars below are the parts of that promise, and the pages you can read to verify it.

  • PILLAR ONE

    Security

    How your data is protected. Authentication, encryption in transit and at rest, row-level isolation in the database, AI provider key isolation, and a security architecture built into the product from the first line of code. Honest about what is live today and what is still on our roadmap.

    See the security posture →
  • PILLAR TWO

    Privacy

    How your data is handled. What we collect, what we do not, who we share it with, the rights you have over it, and a commitment never to sell it or to train AI models on it without your explicit, informed consent. Hard delete is a real hard delete.

    Read the privacy policy →
  • PILLAR THREE

    Honesty

    How the analysis itself stays unbiased. The principles that govern every analysis Kindred produces: steelman every position, show the work, source everything, no political alignment, no nudging. The pillar most products do not have, and the heart of what Kindred means by trust.

    Read the principles →
One idea, three sides

Three pillars, one promise.

Most products treat security and privacy as a compliance checkbox, and trust as a tagline. Kindred treats trust as the whole product. A platform whose job is to help you reason clearly cannot ask you to take its reasoning on faith.

So we publish the security architecture in concrete technical detail, even where that means publishing what is not yet built. We publish the principles that govern every analysis, with the disciplined commitment to steelman every position rather than nudge you toward one. We publish a privacy policy designed for GDPR and CCPA and a true hard delete, because your data is yours.

If any of the three pillars fail, the others do not redeem them. A product that protects your data while subtly steering your conclusions is not trustworthy. A product that gives you fair analysis while leaking your data is not trustworthy. The commitment has to be all three, or it is none.

Supporting documents

Read further.