My work
These public-safe case studies focus on the problem, product logic, and my contribution. Confidential company information and unapproved metrics are intentionally excluded.
Support intelligence
Repetitive support requests consume skilled team capacity, while customer context is often distributed across knowledge and systems.
Design a workflow that classifies intent, retrieves relevant knowledge, drafts a grounded response, and escalates uncertainty to the right human.
A safer path toward faster assistance and more consistent resolution—without treating automation as a substitute for judgment.
Conversational AI
Customer conversations require speed, context, authentication, and empathy. Automating only the conversation layer can create more friction.
Treat voice as an end-to-end service journey: intent detection, identity checks, trusted resolution, fallback logic, and seamless human handoff.
A practical blueprint for voice automation that measures success through resolution and trust—not simply call containment.
Customer intelligence
Preparing customer reviews often means gathering fragmented data, interpreting signals, and manually turning them into an executive narrative.
Use AI to assemble inputs, identify meaningful changes, surface risks and opportunities, and propose a storyline for human review.
A repeatable workflow that moves team effort from information assembly toward customer strategy and action.
Agentic onboarding
Enterprise onboarding involves interconnected milestones, dependencies, follow-ups, and exceptions. When context is distributed, teams can spend more time coordinating work than advancing the customer.
Design an agentic workflow that monitors milestone signals, identifies blockers, prepares context-aware follow-ups, recommends owners, and escalates exceptions instead of acting beyond agreed guardrails.
A coordinated onboarding experience designed to improve visibility and momentum while keeping customer-facing decisions under human oversight.
Customer health
Customer risk rarely appears in one place. Adoption changes, support history, engagement patterns, and direct feedback can remain disconnected until intervention becomes more difficult.
Combine relevant customer signals into an explainable risk view, summarize the contributing evidence, and recommend a next best action for the Customer Success team to review.
A more proactive operating model that helps teams focus attention, understand why a customer may need support, and respond with appropriate human judgment.