Everyone wants to add AI. But most AI features either don't work well enough to ship, cost too much to run at scale, or solve a problem your users don't actually have. I'll tell you which category yours falls into before you commit engineering resources.

I conduct a focused AI feasibility assessment that evaluates whether LLMs, AI agents, or machine learning features will genuinely improve your product. You get a clear yes/no/maybe with technical specifics, cost projections, and a build plan if the answer is yes.

Use case viability — does AI actually solve this problem better than a rules-based approach?

Data readiness — do you have the data needed to make AI features work? What's missing?

Model selection — which LLM or AI approach fits your use case, accuracy requirements, and budget?

Cost projection — what will this cost to run at your current scale? At 10x? At 100x?

Build vs. buy — should you build custom AI, use an API, or integrate an existing AI product?

Risk assessment — hallucination risks, accuracy requirements, regulatory considerations, and user trust implications

Integration complexity — how hard is it to add this to your existing architecture?

Feasibility verdict — clear recommendation: build it, don't build it, or build a simpler version first

Technical specification — if viable, a detailed spec for how to implement it (architecture, models, data pipeline)

Cost model — projected monthly AI costs at different user volumes

Build plan — estimated timeline, effort, and phasing for implementation

Live strategy session — 45-minute call to discuss findings and answer questions

Founders who want AI in their product but aren't sure where to start

Product managers evaluating AI feature requests from stakeholders

CTOs who need an honest second opinion before committing engineering resources to AI