Clear, Practical Guidance for
Product Leaders Building with AI
I help product people navigate AI-specific challenges. I translate complex AI concepts into actionable product decisions, so you know what to build, why, and how to scope it without drowning in technical noise.
Building AI capabilities is one thing. Shipping them in a way that reliably delivers business value is another.
I’ve combined deep product leadership experience with hands-on work building real AI features. That gives me a rare vantage point: I help product teams turn AI capability into production- and scale-ready features with measurable impact, not experiments that die on the launch pad.
Product teams face unique challenges when bringing AI into the roadmap
Unclear problem fit
It's difficult to determine which user problems AI can actually solve versus where traditional approaches work better.
Uncertainty in scoping
It's difficult to determine which user problems AI can actually solve versus where traditional approaches work better.
Pressure to move fast
Teams feel urgency to deliver AI capabilities but lack the frameworks to make confident decisions quickly.
Technical complexity
Leaders must navigate APIs, model behavior, evaluation strategies, and reliability risks—often without deep AI expertise.
Stakeholder expectations
Executives want quick wins, but PMs must balance ambition with feasibility and long-term product health.
To help you confidently bring AI capabilities into your product,
I focus on three core areas
1. Smart Problem
Selection
Identify where AI meaningfully improves the user experience, not just where it’s technically possible. This prevents wasted effort on flashy demos and focuses your team on value-creating opportunities.
2. Practical Scope & Scale with Confidence
AI features introduce uncertainty. I help you translate that uncertainty into a clear, testable, thin-slice MVP your engineering team can ship fast. I help you put the structure in place to scale and operationalize your AI solution.
3. Cost, Reliability & Risk Control
AI can be unpredictable. I help you design guardrails, evaluation strategies, and cost-aware architectures so your feature remains reliable, scalable, and economically viable.
Work With Me. Outcomes You Walk Away With.
I help product leaders operationalize their AI solutions and make them ready for scale.
AI Ops Audit: Is your AI solution ready to scale?
A fast, structured assessment of your AI feature maturity and readiness to scale. We walk through metrics, evals, automations, reliability and economic concerns and identify potential gaps and high priority initiatives for your roadmap.
Outcome: A roadmap to operationalize your AI solution and make it ready for scale.
Advising
3+ months engagement
Video calls
Optional async access
Product & AI
Product process with AI
Product best practices
Opertionalize AI solutions
Fraction Product Leadership
Fixed period engagement
Head of Product / AI roles
Product process implementation
Mentoring
Product leader hiring support
Start Learning
Build the foundations you need to understand, validate, and scope AI capabilities with confidence.
Adaptive AI-Tutor
Learn core AI concepts in plain language, fast. This adaptive AI tutor helps PMs understand AI concepts like RAG, agents, evals, vector databases, and more in under 30 minutes.
AI Feature Validation (Mini Course)
A structured mini-course that teaches you how to validate AI feature ideas before investing time and engineering resources.
Practical product thinking applied to real AI work.
Not theory or hype.
Built & Shipped (Hands-on AI Experience)
I’ve worked hands-on with AI at the application layer building, integrating, and operating AI-powered features with real-world constraints.
Custom GPTs for structured product workflows
Prompt chains using the OpenAI Completion API
Cost estimation & monitoring (per request / analysis / customer)
Vision API integrations for high-fidelity image understanding
Building AI-enabled products is new for most teams. I focus on applying AI at the application layer, combining strong product discipline with hands-on experience to help teams turn AI capabilities into production-ready features.
Mini Case: AI Cost Transparency & Control
Problem: We built an LLM-based analysis that evaluated long-form articles across five major categories, each with multiple sub-checks. This required extensive prompting and multiple model calls. To make responsible product decisions, we needed to understand the real cost of a full analysis run — across input/output tokens and different model choices.
Approach: I designed a lightweight cost-estimation and monitoring layer that made cost per category, per full analysis, and per user visible early. This allowed us to reason about pricing models, compare model choices, and iteratively optimize prompts and checks.
Outcome: Clear scoping decisions, pricing confidence, and guardrails in place before rollout.