Building a generative AI practice that banks can actually put into production.
RAG pipelines, reusable banking accelerators and AI adoption roadmaps, with architectural governance over the AI squads that deliver them.
Banks and credit unions were approving generative AI use cases faster than they could get them through risk review and into adopted use. Bespoke builds repeated the same work on every engagement.
The BFSI generative AI practice: RAG pipelines, banking accelerators and AI adoption roadmaps, with architectural and strategic oversight of the AI development squads, embedding code quality, test automation and design standards into client engagements. AI governance for regulated environments, aligning model behavior, auditability, model risk and compliance with the business case. Advising executives on AI strategy, use case selection and build-versus-buy, and running the change management that turns an approved use case into adopted capability.
The firm's assets and accelerators program for banking, which I lead. Three accelerators to date: one for an identity verification provider, published on AWS Marketplace; a second running in the Myridius environment; and one that turns a single-line business request into a structured epic and story set with compliance, privacy and security guardrails built in.
This continues the reusable assets and IP function I ran for IBM's financial services business in North America from 2017: find where repeatable IP replaces bespoke build, then productise it.
Reusable, pre-governed building blocks are what let a regulated institution move from pilot to production without rebuilding the scaffolding every time.