Smartflow.

The business problem

Credit-agreement onboarding connects complex documents to operational systems. Finastra’s announcement and public demo show Smartflow’s role in that workflow.

The leadership decisions

My focus is the whole operating model: the fields that can pass through, the exceptions that need a person, and the evidence that makes a result usable. I see review effort and adoption as product questions alongside model quality.

The team and delivery

I lead product, engineering and business work in the Data & AI unit, reporting to the CEO. The public Finastra partnership and demo show the integration surface; they do not disclose private customer implementations.

Outcomes and evidence

Marketnode: I report to the CEO and lead product, engineering and business work in Data & AI. Revenue per client grew up to eightfold from the first contract. Smartflow cleared a Tier-1 bank security and risk review. These are owner-reported outcomes; measurement details and independent corroboration are not published here.

My field-level description is: “95%+ of fields straight through, the rest routed to human review.”

Owner-reported outcomes; measurement details and independent corroboration are not published here. Company and team achievements remain company and team achievements.

What I take from the work

An accurate extraction becomes useful when it fits the next operational step. My writing on AI economics explores why exception work and adoption can change the business case.

The economics of an enterprise AI workflow →