AI & Technical question
Ramp is considering an API-based partnership that would expand the product surface area. From first conversation to launch decision, how would you structure the evaluation? Be specific about the product and engineering inputs you would need, such as API coverage, auth model, data flows, SLAs, implementation effort, and ongoing partner dependencies, and how those technical facts would affect whether Ramp should build the integration.
- Ramp
- AI & Technical
- Hard
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What this question tests
Whether you can run a technical partnership evaluation end to end, translating engineering facts like API coverage and SLAs into a clear build-or-partner recommendation.
How to approach it
- Start with the product surface question: what would Ramp customers gain, and does it overlap with something Ramp should own directly versus genuinely extend reach.
- Gather technical inputs with engineering: API coverage against the needed workflows, auth model and how it affects security review time, and data flows including what leaves Ramp's systems.
- Assess operational risk: SLAs on uptime and latency, and what breaks for Ramp customers if the partner has an outage or deprecates an endpoint.
- Estimate implementation effort against maintenance burden, since an easy initial integration with a fragile or poorly documented API creates ongoing partner dependency risk.
- Combine into a recommendation: partner when API maturity and SLAs are strong and the surface is not core differentiation; build when the partner's technical foundation is weak or the surface is core to Ramp's value.
What a strong answer includes
- Explicitly connects technical facts, like SLA strength and API maturity, to the build-or-partner decision rather than treating them as a checklist.
- Flags ongoing partner dependency as its own risk category, not just a one-time integration cost.
- Distinguishes core-to-Ramp surfaces, which lean build, from extension surfaces, which lean partner.
Common mistakes
- Lists technical due diligence items without connecting them back to the decision.
- Ignores ongoing dependency risk and only evaluates the initial integration effort.
Likely follow-up questions
- How would you weight a strong API against a weak SLA history.
- What would trigger re-evaluating the partnership after launch.
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture