You can just use us for the AI
Consider a fintech that has decided it needs better financial crime detection. Fraud is climbing, the compliance backlog keeps growing, and the team knows AI should be carrying more of the load. Now they hit an old question. Do they build it themselves, or do they buy it?
Building looks appealing at first. I spent time recently with several compliance teams who had gone that route. Each had put two or three months into their own investigation agents, with engineers shadowing analysts to learn the work. The results were mediocre.
The reason is not effort or talent. Financial crime is a strange, specialized domain. The typologies keep shifting, and regulators expect every decision to be explainable. Most of the real difficulty lives in a long tail of edge cases that a team only discovers after months in production. A general-purpose agent built in a quarter handles the easy alerts and falls apart on the ones that count. Worse, without a large set of graded examples to measure against, the team often cannot tell how well it is doing at all.
Buying has carried its own weight. Adopting a full platform can feel like a large commitment. You migrate data, retrain a team, change the way people already work, and wait months before anything goes live. For a group that mostly wants better AI in the loop, that is a lot of effort to absorb before any value shows up. So they hesitate, or they talk themselves back into building, and the cycle repeats.
This is the gap the launch is meant to close.
We shipped an API suite for our AI Agents. It lets you call our financial crime agent directly, from inside whatever system you already run. You register an agent, give it a task from a catalog of what Unit21 can do, start an investigation, and get the decision and the reasoning back as data. You do not have to move onto the whole platform to get there. You can use us for the one thing you came for, which is the AI, and leave the rest of your stack exactly as it is.
Consider how that plays out on a normal day. An alert fires in your own system. Your code calls our agent and points it at that alert. A moment later the answer comes back: a recommendation, the evidence it gathered, and a written narrative explaining how it got there. Your system reads that response and acts on it, clearing the routine cases and routing the genuinely suspicious ones to a person. Your analysts never leave the tools they already use. The specialized work happens behind an API call.
Said another way, we become a call inside your system rather than a place you have to move into. That also means you can start small. Point one queue at our agent, watch how it does against alerts you have already worked, and expand from there once you trust it. You are not betting the whole operation on a migration. You are trying one thing, cheaply, and growing it if it works.
Notice what that does to the build-versus-buy question. Teams rarely build because they want to. They build because buying has felt like too much to take on. If a specialized, production-grade financial crime agent is one API call away, the effort of starting drops close to nothing. The fastest way to get good AI working against your alerts used to be a quarter of your own engineering time. Now it is a call to ours.
I want to separate this from a larger idea I hold, because the two are easy to blur together. I believe the long arc of software is that your own agent, Claude or whatever you end up using, becomes your primary way of working, and the tools sit behind it. That is a real shift, and we are building toward it. This release is not that. This is something smaller and, right now, more useful. It makes it trivial to put our financial crime AI to work inside the system you already have.
For a fintech weighing build against buy, the calculation just changed. The hard part was never wanting good AI against financial crime. It was the cost of getting started. Bring that down to a single API call, and the choice mostly makes itself.
Want to see what our financial crime AI agents can do against your alerts? Book a demo.
Kunal Datta is the Chief Product Officer at Unit21. Prior to Unit21, he led the Product team for Checkout at Fast, and prior to that, led the Product teams responsible for automating aerial wildfire safety inspections at Pacific Gas & Electric.
He has a background leading Product teams using AI to automate processes at regulated entities, as well as financial products, machine learning products, web applications, mobile applications, hardware products, and data products. Kunal is a Fulbright Scholar and studied Civil and Environmental Engineering and Music Science Technology at Stanford University.
