Work

The firm does two kinds of work. We advise financial institutions moving AI from experimentation to production. And we build working systems, each one a specific, falsifiable hypothesis about where AI and financial infrastructure are going. The systems generate the operational knowledge the advisory work runs on.

Advisory Practice

AI Strategy for Financial Services Leaders

We work with banks, payment companies, crypto firms, and financial institutions to move AI from experimentation to production. The practice is built on two decades of infrastructure experience: building Visa’s real-time payment architecture and serving as CTO at a digital asset bank through one of the most volatile regulatory periods in modern finance.

Engagements are designed for senior leadership teams across banking, payments, and digital assets — executives deciding where to invest in AI, how to govern its deployment, and how to build advantage without breaking regulatory obligations.

Engagement types

Executive AI Strategy Briefings

Half-day sessions for C-suite and technology leadership. Competitive landscape, regulatory framework, use case prioritization, and 90-day action planning.

Governance Framework Development

AI risk committee charters, model risk management approach, vendor accountability frameworks. Designed for board presentation and regulatory examination.

Implementation Advisory

Ongoing strategic support during the first 90 days of AI deployment. Vendor evaluation, use case scoping, board communication strategy.

Team AI Enablement

Hands-on working sessions for engineering and product teams adopting AI tools and building with GenAI APIs.

Working Systems

Each system starts as a hypothesis: a statement about the near future that could turn out to be wrong. We build the system to find out. What survives contact with real users and real constraints becomes advisory material; what doesn’t gets retired.

Financial Infrastructure

AI Systems

MigrateForce

AI Migration Engine

Hypothesis

Enterprise AI will not be bottlenecked by models. It will be bottlenecked by the legacy systems models cannot reach.

A high-speed, AI-driven engine for enterprise application migration. Tests whether agentic systems can compress multi-year modernization programs into months — the precondition for AI adoption at institutions running decades-old cores.

Versalist

AI Evaluation Platform

Hypothesis

Static benchmarks will not predict production performance. Challenge-driven evaluation — real systems under real constraints — will become how institutions assess AI capability.

A challenge platform where engineers build and evaluate working AI systems, from single tasks to multi-agent architectures. The evaluation methodology it generates feeds directly into our assessment frameworks for AI in high-stakes financial decisions.

ActsAsGeek

Agentic Workflow Orchestration

Hypothesis

The scarce skill in agentic work is command, not construction. A single operator will direct workloads that once required a team.

An agentic command center for orchestrating, monitoring, and scaling AI workflows as a solo operator. A live study in what directing autonomous workers actually requires — the coordination patterns apply directly to financial operations.

SocialCRM

AI Visibility

Hypothesis

Distribution is moving from search ranking to AI recommendation. Visibility inside generative systems becomes a channel institutions must manage like any other.

An AI system that measures and optimizes content visibility across search, social, and generative AI platforms. Informs how we think about AI-mediated distribution in financial services marketing.

How we choose these hypotheses — the method behind the work

We Value Your Privacy

We use cookies to enhance your browsing experience and analyze our traffic. By clicking Accept, you consent to our use of cookies.