• Embed directly with issuer and acquirer clients to understand their operational workflows, surface pain points, and identify where AI-driven automation or intelligence creates measurable business value.
• Design and build rapid proof-of-concept prototypes - agentic pipelines, conversational interfaces, analytical tools - scoped deliberately to test a specific business hypothesis, not to be production-ready.
• Translate ambiguous client requirements into concrete, testable problem statements; push back on requirements that are too broad to validate or too narrow to generalize.
• Run structured validation sessions with customers using working prototypes, gather qualitative and quantitative signal on whether the PoC addresses the stated need, and document what it does not.
• Synthesize field observations into product briefs: scoped requirements, edge cases discovered in the wild, constraints the platform team would not have known from a desk, and a clear go/no-go recommendation.
• Partner with product managers to ensure PoC findings directly inform roadmap decisions - requirements that survive validation become backlog items; those that do not get killed before investment is made.
• Maintain a working knowledge of the AI capability landscape (LLMs, agentic frameworks, retrieval-augmented generation, structured data reasoning) broad enough to know what is feasible in a six-week PoC versus

what requires a platform build.

• Identify patterns across client engagements that suggest platform-level investment - repeatable needs that should become product features rather than one-off prototypes.
• Represent technical feasibility in pre-sales and discovery conversations; give customers and internal stakeholders an honest read on what AI can and cannot do for their specific workflow today.
  • Mentor engineers and analysts on the team on customer discovery craft - how to ask questions that reveal unstated requirements, how to scope a PoC to answer one question well, and how to present findings without overselling.