Financial services

AI where the audit trail is a requirement.

Most AI work in financial services does not fail on model quality. It fails at the point where someone has to explain, months later, what the system did and why it was allowed to. We build for that conversation first.

A Massachusetts company doing integration work since 2011. Where we are and who we contract as.

Who this is for

Four kinds of institution, with four different reasons the integration is the hard part.

Banks and credit unions The core is the constraint. Work lands as document handling, servicing correspondence, reconciliation, and internal search across systems that were never designed to be queried together. Core banking · Document stores · Servicing
Payments and fintech Speed is the product, so the governance has to be part of the request path rather than a batch job that runs afterwards. Dispute handling, onboarding review, and merchant or partner operations. Ledgers · Dispute flows · Partner APIs
Insurance The work is evidence-shaped: submissions, claims files, medical and property documents, and a decision that has to be reconstructable against the file as it stood at the time. Policy admin · Claims · Document intake
Asset and wealth management The line between analysis and advice is a regulatory line, not a design preference. Research summarization, operations, reporting, and client correspondence, with that boundary built in rather than added later. Portfolio systems · Reporting · Correspondence

The four constraints

What actually decides the architecture

In a regulated institution these are not preferences to trade off against model quality. They are the requirements, and they are settled before a model is chosen.

1Evidence Every model call, its inputs, the decision, and the actor recorded as a structured audit event, with sensitive fields redacted. Record-keeping rules already reach agent-generated records: SEC Rule 17a-4 and FINRA Rule 4511 govern the retention and integrity of the record, and the EU AI Act's automatic event-logging obligations for high-risk systems apply from August 2026. What a defensible agent audit trail looks like.

2Classification Which data class a request carries decides which provider may see it, and that decision is made by policy rather than by the feature that happens to need an answer. Public API, enterprise endpoint, and self-hosted are different answers to different classifications. Hosted, private endpoint, or self-hosted.

3Portability The model provider is a configuration choice, not a dependency. Business logic expresses a capability and its constraints; the provider sits behind an adapter and can be added, replaced, or removed without touching the feature. What portability actually requires.

4Confirmation Nothing that moves money or commits the institution executes on model output alone. The gate sits on execution rather than intent, so the system is free to assemble, compare, and draft, and a person approves the commit.

Why we can say this rather than promise it

We built these four constraints into our own product before selling them as a practice, which is a narrower claim than a case study and a more checkable one.

MITRA, the platform this company builds, runs model inference through a system designed so we are never locked into one AI provider: a declarative registry, one adapter per provider, and a rule that a provider which is unconfigured or unimplemented says so plainly instead of quietly switching to a different one. Several providers sit behind that interface today, including a self-hosted local deployment and an enterprise-hosted endpoint serving the same open-weight model family, which is what makes the deployment-model argument on this page something we have tested rather than something we have read.

Every model call is written to an append-only audit trail as a matter of architecture rather than configuration, and the platform's own internal tooling chains those records so that a modification to history is detectable. Our cloud-neutral architecture standard requires business logic to depend on provider interfaces rather than on any cloud vendor's SDK, for the same reason the model layer is abstracted: the thing you cannot swap is the thing that eventually sets your terms.

None of that is a claim about outcomes at your institution. It is a claim about how we build, which is the part you can inspect before you engage us.

What we will not do

These positions cost us work occasionally. They are also the reason the product side of this company is credible, so they are stated here rather than discovered at contract stage.

We do not ship an agent that can take a consequential action without a human confirmation and an audit record.
We do not route data to a provider that is not approved for that data's classification because the provider produces better results. Failing the request is the correct outcome when no authorized alternative exists.
We do not grant training rights on client data, to anyone.
We do not describe country-based routing as a data-residency guarantee. Choosing a regional provider selects a provider; residency is a contractual and infrastructural property, and conflating the two is how a compliance claim becomes untrue.
We do not take work that leaves a client dependent on us to operate it.

Nothing on this page is legal, regulatory, or compliance advice. Which rules bind your institution, and how, is a question for your counsel and your examiners.

How an engagement starts

The first conversation is a briefing, not a pitch. We want the process, the systems it touches, the policy that governs it, and who has to sign off. What comes out of it is a written view of whether the work is worth doing, what it would take, and whether we are the right team. Often the answer involves less AI than expected, and we say so in week one rather than in month six.

Then a compliance review before anything is built, a pilot with a success rubric defined in advance, and a handover that includes documentation, runbooks, and the training to run it without us.

Read how an engagement runs in detail, or the reference architecture these engagements deliver against.

Bring us the one that stalled.

The integration nobody wants to own, or the pilot that never got past the security review. We would rather tell you honestly whether we are the right team than win the work and find out together.