Measuring Fit, Not Guessing
Product-market fit is the single most important metric for product teams, yet it is typically assessed through qualitative signals: customer interviews, NPS scores, and usage metrics. Sentient OS makes product-market fit computable. The DNA layer encodes products and audiences in the same 48-dimensional vector space. Fit is the distance and direction between product vectors and audience vectors - a mathematical measurement, not a qualitative assessment.
Real-Time Fit Measurement
Traditional fit measurement is periodic - quarterly surveys, annual reviews, post-launch analyses. Sentient OS measures fit continuously. As the Sensor captures behavioral signals and the DNA layer updates persona and product vectors, fit scores change in real time. If a product update shifts the product vector closer to a specific archetype, fit with that archetype improves measurably. If a market shift moves audience vectors away from your product category, fit erosion is detected immediately.
Price-Feature-Audience Alignment
Market Fit and Psychographic Layer together provide a complete picture of price-feature-audience alignment. Is the product priced correctly for the audience? Do the features align with audience priorities? Is the competitive positioning differentiated in ways the audience values? These are not separate analyses requiring different tools - they are integrated dimensions of the same vector space, computed simultaneously and presented in the Command Center.
Competitive Intelligence
The DNA layer encodes competitor products in the same vector space as yours. This means competitive distance is computable: how far is your product from the closest competitor in the eyes of each archetype? Where are you differentiated? Where are you interchangeable? This competitive intelligence is real-time and behavioral - not based on feature checklists or pricing tables but on how audiences actually perceive and respond to each product.
New Product Validation
Before launch, product teams can compute predicted fit by placing a new product vector in the existing space and measuring distance to target audience vectors. This is not a prediction based on historical analogy - it is a geometric computation in behavioral space. If the new product vector is close to audience vectors that convert, fit is strong. If it sits in an unpopulated region of the space, the product may be innovative but the audience may not exist yet.
Feature Prioritization by Impact
Product teams can use Conversion Modeling to understand which product features drive conversion for which archetypes. This replaces "customers say they want feature X" with "feature X produces +12% conversion lift for the Value Optimizer archetype and -3% for Impulsive Aesthetes." Prioritization becomes data-driven at the archetype level.