Make (Integromat)
Vannus records that this vendor routes across several model providers — not captured by one. Every finding below is quoted to the vendor’s own document, or marked not disclosed where the vendor publishes nothing.
Visual automation platform for building complex workflows without code
Common AI providers include OpenAI, Gemini, and Anthropic Claude. Make's AI Provider connects to LLMs without needing an account with a provider.
Training and retention posture varies by plan. What we publish above describes the vendor’s default plan; enterprise, team and API agreements frequently differ, often materially, and a contract can override the published default entirely. Check your own plan and contract before relying on this row.
This is a separate question from the grade above. The grade measures resilience — whether the tool endures and whether you could leave it. This describes who controls the vendor. A tool can score modestly on one and strongly on the other, and many do.
On U.S. CLOUD Act reach specifically: the statute reaches a provider subject to U.S. jurisdiction over data in its possession, custody or control. Corporate control is a strong indicator of that and it is what we can evidence from published documents — but it is not the whole test. A company founded outside the U.S. can still contract through a U.S. entity or run substantial U.S. operations. Treat this as a starting point for your own review, not a legal determination, and take advice on anything that matters.
The vendor's published or catalog-recorded posture — the concrete facts this grade is built from. A full audit verifies each against the vendor's current documentation.
Taken from the vendor’s own published material. Vannus does not hold these reports and has not reviewed their scope or dates — ask the vendor for the current report before relying on any of them.
Vannus publishes a nine-dimension trust framework — data sovereignty, training privacy, compliance posture, operational resilience, exit portability, and more. The heaviest criterion asks whether the tool builds its own AI or resells someone else's model; where the vendor discloses it, the grade cites the vendor's own documentation. No paid placements — scoring is walled off from affiliate revenue. See the methodology →