Case management AI: five models compared

Key takeaways

1. Most case management AI vendors now offer an audit trail and a human decision; those are table stakes, not a way to choose.
2. The questions that separate the five models are structural: who owns the program, where the work compounds, and what survives a model change.
3. All five models leave the program, the vendors, and the exam accountability where they were: with you.
4. Infinite is not a tool in your queue: it runs its own live compliance program on a network, and a counterparty cleared once is cleared for everyone.
5. First-time counterparty reviews that take around 30 days under one-off manual review close in 1–2 days on Infinite.

One term, three different products

"Case management" is the system of record for compliance work: alerts become cases, cases become decisions, decisions become filings, and every step leaves a record an examiner can read. "Case management AI" is whatever does the assembly work inside that loop: pulling the counterparty file, running screening, reading documents, drafting the narrative, recommending a disposition.

The term now covers three products that have little in common besides the demo. It can mean a module inside a monitoring platform your team already operates. It can mean a standalone agent that researches an entity and hands back a report. And it can mean a managed service where a vendor's agents and analysts work your queue for you. Buyers who compare them by watching each one draft a SAR narrative learn nothing, because all three draft a fine narrative. The differences are underneath.

The five models

AI-native BPO replacement. Bretton AI states the shape plainly: agents that work inside your existing compliance systems, layered over your data and policies, with a managed service that keeps experts in the loop. You buy capacity in your queue without headcount. The program, the case system, the providers, and the examiner's questions remain exactly where they were.

Research agents. Parcha was the clearest example: agents that ran KYB and enhanced due diligence research and returned a documented file. As of September 14, 2026 the company operates as Grep AI, a platform for research-intensive knowledge work in general, with compliance as one use case. A research agent makes the analyst's file richer. It does not change who runs the program.

Platform with agents inside. Unit21 sells the case system itself, with monitoring, screening, filings, and agents that triage alerts and draft investigation summaries inside it, all operated by your compliance team. You buy a better operating system for the queue. The queue is still yours to staff, and the same counterparty is still reviewed again at the next institution.

Build your own. Retool is a fast way to a case tool that matches your process exactly, with approval gates, an attributed action log, and agents on any model. You buy the interface and the plumbing. You also take on a software product whose every change, from a new provider to a model upgrade to an examiner request, is now yours to ship.

Traditional BPO. The outsourced analyst team is what the other four promise to replace, and it is still the most common answer to a growing queue. You buy people, priced per analyst or per case. Cost scales with alert volume, and alert volume grows faster than the business that produces it.

Table stakes are what every vendor can say. The question is what your program looks like after the contract.

What actually separates them

Audit trails, per-decision logging, and a human on the final call are now common to every serious offer in this category, including ours. Choosing on those is choosing on nothing. The questions that separate the models are structural:

• Who owns the program? Policy, thresholds, providers, quality control, and the exam. In all five models the answer is you. The vendor is in your queue, not accountable for your program.
• Where does the work compound? A review done in your queue helps your queue. It does not help the next institution the same counterparty walks into, so the industry re-reviews the same entities again and again.
• Can the human gate be configured away? Under volume pressure, an optional review step eventually is. The gate has to be structural.
• What changes when the model changes? If the guarantee is a property of a model version, it moves when the model does. If it is a property of the system (the tools, the gates, the record), it does not.
• Where does the compliance work sit relative to the money? Adjacent to the payment, in a separate stack, or fused to the account and the rail so screening runs before funds move.
• What does the queue cost as the book grows? Per analyst, per case, per model call, or as a program whose marginal review gets cheaper because someone already did it.

Where Infinite sits

Infinite is not a sixth tool for your queue. It is a network that businesses transact on, and Compliance AI is the system that runs the network's own compliance program: domain-tuned agents, the providers and tools they operate, the context of the account graph, human review gates, and a complete audit trail, one system. It runs Infinite's KYC, KYB, KYT, and AML work today, and a named analyst signs every decision. The AI makes no decisions, files no reports, hides no actions, and changes no policy.

Two properties follow that no vendor layered over someone else's stack can offer. The program is fused to the money: every counterparty is screened before funds move, and a review is a state on the account you can query. And the work compounds: a counterparty cleared on the network is cleared for every business on it, which is why first-time reviews that take around 30 days under one-off manual review close in 1–2 days. Banks, fintechs, and platforms run their programs on it in different shapes, fully managed, co-operated, or self-operated on Compliance AI over their own systems and providers. Managed Compliance is the fully managed form. Which shape fits is a conversation, not a checkout.

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