I put AI to work.
Inside real workflows.

How I start close to the customer

I begin with the real workflow. I clarify the business problem, identify where AI can help, and work with engineering to shape a solution that fits the customer’s systems and operating environment. I stay involved through validation, delivery, and adoption.

My AI product and delivery experience

• Enterprise AI agent platforms. I manage product and programme delivery around agent runtimes, agent and tool registries, remediation channels, approval workflows, and enterprise integrations.
• Document intelligence and credit workflows. I work on OCR, data extraction, financial document processing, review and approval journeys, and downstream system integration.
• Knowledge retrieval and enterprise data. I plan product capabilities around vector databases, table schemas, data dictionaries, and retrieval-augmented generation for domain-specific questions.
• AI-assisted onboarding. I shape conversational onboarding and define what a useful customer interaction should achieve.
• AI discovery. I evaluate use cases and weigh technical feasibility, customer value, and delivery effort.

How I take a use case into delivery

I help define the target workflow, data requirements, integration boundaries, human review points, and success criteria. I work with engineers to make implementation choices concrete, keep stakeholders aligned, and plan releases that produce useful learning before expanding scope.

How I judge whether it works

I agree the measures with your team: task completion, result quality, exception handling, time saved, adoption, and operating cost. I include evaluation and human oversight in the product plan from the start, and establish a clear owner and path into day-to-day use.