Real outcomes: challenge, approach, and results.
In short: We helped scale a buy now, pay later style lending product with low defaults and repeat usage.
Scale lending with low default rates and repeat usage while enabling merchants to offer BNPL (buy now, pay later) without carrying credit risk.
In short: We built and ran AI that improves the quality of trading decisions across many symbols.
Reliable, production-grade ML that improves entry quality and regime awareness with a single pipeline over 100+ symbols.
In short: We helped deliver faster, transparent lending with digital applications and AI-assisted underwriting in the US and Nigeria.
Faster, transparent lending with digital application and AI-assisted underwriting in the US and Nigeria.
We start by understanding your problem and constraints, then we agree on scope, steps, and outcomes. When needed we do discovery first, then a pilot or full build with clear milestones. What we need from you: access to data, subject matter experts, and decisions when we hit trade-offs. Our engagement types page spells out typical project types and price ranges.
Our team comes from fintech and lending. We have built and operated systems that process millions of dollars in loans and that run in production every day. We bring that experience to your project so you do not have to reinvent the wheel.
We are from the team behind a VC-backed fintech. We run lending; we build the software that powers it. That means we understand not only how to architect and ship systems but also how they are used in the real world: by operations teams, by risk, by compliance, and by the end customer. When we propose a solution, we are thinking about how it will perform in production, how it will be maintained, and how it will evolve as your business grows. That real-world experience is what sets us apart from pure-play dev shops that have never run a loan book or stood behind a decision engine in production.
Ready to see how we would approach your initiative? Book a discovery call and we will outline what is similar to these case studies and what we would recommend for your context. No deck needed.
Not every project needs an external partner. If you have a large in-house team, clear scope, and plenty of time, you may build everything yourself. But many banks, credit unions, and fintechs find that they are short on one or more of those: they have a small engineering team, scope is still fuzzy, or they need to move fast to hit a regulatory or market window. In those cases, a partner who has done similar work before can de-risk the project and shorten time to value. The right partner brings domain knowledge (so you do not have to teach them lending or ML from scratch), delivery discipline (so you get milestones and clarity, not open-ended engagements), and the ability to scale up or down as your needs change. We offer discovery and strategy engagements to align on scope, proof of concept and pilot engagements to validate an approach, and full build engagements when you are ready to go to production. We also offer retainer arrangements for ongoing support and iteration after launch. Our engagement types page describes the bands (e.g. discovery, pilot, build, retainer) and typical investment ranges so you know what to expect before we have a conversation.
The case studies above are examples of the work we do. We organize our services into five areas so you can see where we add value. AI and ML in banking covers customer service automation (chatbots, personalization), back-office ML (underwriting, operations), and ML that actually ships in production. Modernization covers legacy and homegrown system upgrades, digital transformation without full rip and replace, and API and integration strategy. Advisory and staffing covers strategic advice and roadmap, embedded or dedicated technical capacity, and fintech-savvy talent. Process automation covers operations and workflow automation, compliance and reporting, and internal process digitization. Discovery and strategy covers problem validation and scope, build vs buy and architecture, and the path from discovery to delivery. Each of these has a dedicated service page on this site with more detail and outcomes. We recommend reading the case study that fits your situation and then exploring the relevant service page. If you are not sure which fits, a discovery call is the fastest way to align: we will ask about your goals and constraints and point you to the right place.
Case studies show what we have delivered; the next step is to see how that maps to your project. We recommend reading the case study closest to your situation, then exploring the corresponding service page (e.g. AI and ML in banking for ML decision systems, or discovery and strategy if you are still defining the problem). Our how to choose a fintech development partner and how we scope lending and ML projects posts add more context on what to expect from a discovery call and phased delivery. If you are ready to talk, contact us or book a discovery call; we will tell you what is similar to these case studies, what is different, and what we would recommend for your context.
