The investment process has been engineered for decades. The client-facing side still reads documents by hand.
DDQs answered from memory and old files. Reporting cycles measured in weeks. Relationship managers spending client-facing hours on assembly work. The information these functions run on is unstructured — which is exactly why they were never engineered, and exactly what AI now reads.
Financial institutions have spent the past two years piloting AI, and most of the attention has gone to the investment and risk functions. The faster payback is usually elsewhere: in distribution, reporting, and client service, where senior professionals spend a striking share of their time assembling documents rather than exercising the judgement they were hired for. Investor-relations teams still describe diligence response as the most operationally punishing part of fundraising — despite a market with more than thirty tools selling into exactly that workflow.
That vendor abundance is the tell. The question facing a COO is no longer whether the technology works. It is which workflow to engineer first, with which tool — bought or built — under which data and governance constraints, adopted by a team whose every output may be read by a client, a regulator, or both. That is a judgement problem, and it is poorly served by anyone with a product to sell.
We are based in Basel, work in the regulatory environments our clients answer to, and treat governance as an architecture decision made early — not a compliance document written after the fact.
Four places AI pays back on the client-facing side of a financial institution.
RFP and DDQ response
Institutional questionnaires drafted from your approved record — prior submissions, compliance language, current data — for your experts to review rather than write. The team that wins mandates stops losing its weeks to the paperwork that accompanies them.
Client and investor reporting
Reporting cycles compressed from weeks to days, with commentary drafted against the numbers rather than assembled around them. Few asset managers believe their reporting meets client expectations; most agree it wins or loses mandates. That gap is an engineering problem now.
Relationship-manager leverage
Meeting preparation, portfolio review assembly, suitability documentation — the hours of gathering that precede every client conversation, done before the RM sits down. The client gets more of the person they chose the firm for. The firm gets capacity without headcount.
Onboarding and file assembly
KYC packs, credit files, claims documentation: the document-heavy assembly work where institutions lose days and applicants lose patience. AI extracts, validates, and flags inconsistencies before a human reviews the file — review by exception, not by default.
The structural problem behind a DDQ is not unique to finance: a regulated supplier answering institutional questionnaires from years of prior submissions, where the language must be exact and the expert should review rather than write. We engineered precisely that motion for a €160M life-sciences supplier responding to pharmaceutical procurement tenders — four years of submissions, quality agreements, and regulatory filings indexed into a knowledge base an AI agent drafts from.
In wealth itself: we designed the document-intelligence architecture behind a cross-border wealth platform — six asset-document classes parsed into a unified portfolio view, with PII and financial data separated at the storage layer so the AI operates identity-blind by design, not by policy. Privacy architecture before any code.
Most engagements start with a four-week Revenue AI Audit inside your distribution and client-service operation: we interview the team, shadow the response, reporting, and preparation flows, and map where senior hours go to assembly. You leave with a ranked list of use cases with modeled payback, a clear read on your data and governance posture for each, and a buy-versus-build recommendation — we are independent of every vendor in this market, including the thirty selling DDQ automation.
Where we build, the data architecture comes first: what the AI may read, what it may never see, and how that separation is enforced structurally rather than by policy. Then we stay embedded through the first live cycles until your team trusts the output without us in the room.
The partner who scopes the engagement runs it.
Bring us the questionnaire that ate last quarter, or the report that ships late every month.
Twenty-five minutes. We'll tell you whether the pattern transfers to your shop — and if it doesn't, we'll say so plainly.