AI Product Strategy

Successful AI products start with a crisp problem statement and success metrics—not a model name. We map user journeys, failure modes, and data availability, then score use cases by impact, risk, and feasibility. That yields a prioritized backlog where each item has a cost envelope and an evaluation plan.

For retrieval-augmented generation (RAG), we design data pipelines, chunking, embedding strategies, and freshness guarantees. For agents, we define tool boundaries, timeouts, and human escalation paths so production behavior stays predictable.

Shipping means monitoring quality over time: offline evals, online A/B tests, and red-team scenarios. We set up dashboards for latency, token usage, error rates, and user-reported issues. When models or data drift, we have rollback and versioning so you can revert safely.

Privacy and compliance are woven in—data minimization, retention, regional constraints, and audit trails for regulated industries.

You leave with a living AI playbook: architecture diagrams, API contracts, eval suites, and operational runbooks. Engineering teams can iterate without every change becoming a science project.