ai product feasibility advisor

The problem

Building AI features is cheap, but building viable AI products is expensive. Founders often struggle to distinguish between a "retention hook" and a "monetizable value-add," leading to wasted burn on features that users won't pay for or tech moats that don't actually exist.

What it does

• Evaluates product ideas against the AARRR funnel to determine if they are strategic entry points or dead weight.
• Benchmarks features against global SaaS leaders like Notion, Lattice, and Guru to validate market demand.
• Analyzes technical moats beyond simple API calls, focusing on integration ecosystems and data standardization.
• Quantifies monetization potential by calculating time-saved value against regional pricing anchors.
• Identifies global compliance risks including GDPR, CCPA, and PII data residency constraints.

Frameworks & tools

PLG (Product-Led Growth), AARRR Funnel, GDPR/CCPA Compliance, SaaS benchmarks (Lattice, Linear, Asana).

Why this beats prompting it yourself

This skill forces a brutal, unvarnished 6-module audit that simple prompts lack. It bypasses AI optimism by applying "Iron Laws" regarding hook value and integration complexity, ensuring you don't build features that are easily Sherlocked by platform giants.

Use cases

• Vetting a new AI automation workflow before committing engineering resources.
• Determining if a feature should be a free tier hook or a paid Pro-plan upgrade.
• Evaluating the cross-region PMF for an HR Tech or productivity tool entering the EU or NA markets.
• Auditing the technical defensibility of a data-aggregation feature.

Known limitations

Requires specific descriptions of the feature or workflow to provide high-density feedback. Reports range from 1200 to 1800 words and require high information density inputs.