Helping senior payments executives de-risk investment decisions in a landscape that doesn’t wait.
AI has compressed payments innovation cycles from years to quarters. A wrong call is more expensive now, and the impact shows up faster.
That's where operator experience matters. We staff each engagement with senior payments operators who have run these exact build, buy, and partner decisions inside major financial institutions, not just advised on them. No junior staff. No time spent getting people up to speed.
We work with a limited number of clients at a time, so every engagement gets direct involvement from start to finish, with our thinking focused on your problem rather than spread across a larger portfolio.
Before your organization commits to a build, a platform, or a partner, the critical assumptions get tested. We work alongside your team in your language and aligned to your goals, because the right answer has to fit how the organization actually operates. We identify what has to be true for your proposed direction to work, then test it against the evidence. How we get there adapts — the compliance landscape, stakeholder dynamics, and internal complexity of a large institution all shape the path. What doesn't change is the output: a clear recommendation your leadership team can act on, whether that means moving forward, changing direction, or stopping before the investment grows.
Payments executives typically come to us with one of four questions. They're trying to figure out how to invest in AI in a way that actually holds up inside a large financial institution. They're deciding whether something is worth building before the organization commits significant resources around it. They're evaluating whether they have the right partner, or whether a better path is to build or step back entirely. Or they're trying to attract the technical talent their strategy depends on but can't seem to hire. The practice areas below map directly to those questions.
AI is one tool in the payments innovation toolkit, not an end in itself. The work is evaluating which AI applications are worth pursuing, running structured proofs-of-concept, and making the build-buy-wait call before vendor commitments lock in a direction.
From digital wallets and embedded finance to real-time payments and next-generation card products. The job is defining what to build, designing the experiment that validates it, and determining whether to proceed before the org commits resources to it.
Mapping the fintech and technology ecosystem around a specific payments opportunity, identifying high-value alliance candidates, and structuring the evaluation before a contract is signed. The costly misalignments in partnerships almost always surface after the deal closes, not before.
Payments and technology capability doesn't build itself. At some point every organization has to decide whether to hire for what they need or bring it in from outside. Making that call means structuring teams, defining the talent profile, and establishing the operating model before headcount decisions are made.
Client names withheld per engagement confidentiality.
A regional bank's disputes process ran on manual review and tribal knowledge, slow, error-prone, and costly when mistakes meant money lost. Rules changed often enough that agents working from memory or outdated training were routinely behind. We designed and delivered an agentic AI proof-of-concept, selecting optimal open-weight LLMs and evaluating retrieval-augmented generation approaches to minimize hallucination on proprietary policy documents, to test whether AI could actually fix the accuracy problem instead of just adding another tool.
The stated problem was attrition: subprime customers weren't staying with the company, going inactive or leaving altogether faster than expected. The real problem was upstream. Primary research uncovered the behavioral triggers behind why prime-eligible customers were churning, and identified the signals that needed to be captured at time of application to find graduation-ready customers earlier. Those findings shaped new data science model features across underwriting, fraud, and risk decisioning.
The business needed to add services beyond the core card product to stay competitive for small business owners, fast, in a market where venture-backed startups were shipping faster than any internal roadmap could match. The obstacle wasn't a shortage of startups to evaluate. It was that large, regulated financial companies and fast-moving, venture-funded startups didn't know how to work with each other, and no framework existed to decide whether to partner, white-label, or build internally.
A major bank trying to hire the data scientists and engineers its AI roadmap depended on was losing every comparison to tech companies. The gap wasn't compensation alone. It was brand, visibility, and how candidates pictured the day-to-day experience of working there. Standard recruiting tactics weren't moving the needle because the problem wasn't recruiting, it was how the organization showed up, or didn't, in the communities it needed to recruit from.
A leading payments network was trying to define the future of its merchant data infrastructure but had no shared view of priorities across regions and too many competing internal voices to move forward without external synthesis. We led global stakeholder research and strategy for that initiative, engaging 50+ stakeholders across regions, synthesizing interviews, product roadmaps, and internal documentation to surface product priorities, pain points, and future vision.
We start with a focused working session. No pitch, just problem definition. We want to understand what decision you’re trying to make, how you’ll know if it’s been made well, and what getting it right is worth to the business.