Your compliance stack detects. It doesn’t investigate.

Thirty minutes on your compliance operation — alert volume, false positive rate, where the hours concentrate, and what your next exam or audit looks like.

If there’s a fit, the next step is a paid three-week Compliance Automation Diagnostic: structured interviews, a full workflow review, and a measured baseline of where your program actually stands. No generic pitch deck. No obligation.

A paid three-week independent assessment of your AML operations, delivered as a dated, sourced document you can keep in the exam file.

It produces something most firms have never had: a measured baseline. Not estimates — actual alert volume, actual false positive rate by rule, actual hours per case, actual time from determination to filing, and a clear statement of which thresholds have documented rationale and which don’t.

It ends in a fixed-scope, fixed-price implementation proposal, so there’s no second discovery process. 50% credits toward the build if you proceed within 90 days.

Some firms take the assessment and act on it internally. That’s a legitimate outcome, and the reason it’s priced as real work rather than as a sales step.

Both — because under US law they’re frequently the same thing. A licensed payment company and a digital asset platform typically register with FinCEN as MSBs, run BSA/AML programs against the same pillars, and file SARs under the same 30-day deadline. What differs is the data.

Most compliance stacks handle fiat transaction data well and go blind at the chain boundary. We work across both — which matters more every quarter, as stablecoin flows land in companies that were never built to investigate them.

Into your environment, and nowhere else. Systems run in your cloud tenancy. Models are accessed through AWS Bedrock or Azure OpenAI inside that tenancy — customer data is not sent to third-party model providers and is not retained for training. We document the full data flow before any build begins, in a form your sponsor bank’s third-party risk review can evaluate directly.

The April 2026 interagency guidance that replaced SR 11-7 — SR 26-2, OCC Bulletin 2026-13, FDIC FIL-15-2026 — puts generative and agentic AI outside its scope, with a request for information still to come. Your model risk function has an open question and no supervisory answer yet, so we document against the framework you already run.

Every system ships with a validation pack: model inventory entry, documented intended use and limitations, the evaluation methodology and results against your own case set, prompt and model version control, and a change log that triggers revalidation when either moves.

This matters more than it sounds. A managed AI vendor can update its model without telling you — leaving your validation covering a version you’re no longer running. Systems built here are model-agnostic and run in your environment, so your MRM function controls the version and the change cycle.

To be explicit about independence: this documentation is first-line evidence for your independent validation function to review. It is not a substitute for independent validation.

Every system is built for your firm — not a shared platform, not a generic tool with your logo on it. It runs in your environment, integrates with the tools you already use, and ships with full documentation and training. Your team owns it completely. No platform subscription, no lock-in.

Every determination the system produces cites its source, and every step leaves an audit trail. Filing decisions stay with your BSA Officer, documented exactly as they are today.

No. You own the system outright — the code, the configuration, the documentation. That doesn’t change and there’s no platform subscription.

What’s optional is whether I keep operating it. Models get deprecated, prompts need retuning, regulations move, and your evaluation evidence needs refreshing for the file. Most firms would rather I handle that than build the capability in-house. If you’d rather take it over, I’ll document handover and step away.

Minimal. After the first call, we handle the technical work. Most builds require 3–5 hours of your team’s time across a few weeks for workflow input and review. You stay focused on your product and your users. We come to you only when a decision requires your judgment.

We measure against your baseline from day one, so ROI is concrete rather than claimed. The diagnostic establishes that baseline — alert volume, false-positive rate, hours per case, time-to-file — before anything is built. Most firms have never measured these, which is why the diagnostic is useful whether or not you continue with us.

The common wins show up within the first month: fewer false positives reaching analysts, faster case throughput, and shorter time from determination to filing.

Most AI tools fail for one of two reasons: they solve the wrong problem, or nobody adopts them. We avoid the first by starting with a specific workflow that’s already costing you measurable hours. We avoid the second by building around how your team actually works — and staying through adoption before we step away.

The deeper reason off-the-shelf tools underperform here is that they sit on top of fragmented data. Detection quality is limited by what the system can see, and most stacks can’t see across fiat and on-chain in the same case. That’s the layer we build.

Primarily US. We take selected engagements in English-operating markets where we can read every artifact the system produces — including the UK, Ireland, Canada, and much of EU fintech, where compliance functions run in English.

If your filing language isn’t English, we’ll tell you on the first call rather than three weeks in. In that case we can still build triage, KYB review, screening adjudication, and on-chain context — everything except regulator-facing narrative drafting.