Figr Design Blog

AI demand forecasting uses behavioral data to predict feature adoption, infrastructure load, and support volume so teams allocate resources based on reality, not guesses. It reduces waste and outages by learning drivers (seasonality, market shifts, cohorts) and improving forecasts over time so product, design, and ops decisions carry less risk.

Structured courses beat scattered tutorials because they build real problem-solving frameworks, not just feature tricks and pairing that learning with practice projects is what turns tool familiarity into usable prototyping skill.

Templates save time by giving you solid, familiar UI building blocks so you can focus on the unique parts of your product find them in Figma Community, company design systems, independent freebies, and AI tools like Figr then adapt and quality-check before you ship.

Share prototypes like sensitive intel use passwords/expiration/view-only plus NDAs/terms, scale up to SSO/RBAC/audit trails when risk is high, and be ready to detect and respond to leaks.

R/VR prototypes need to be built and tested in spatial tools (not flat 2D mockups) otherwise you get misleading confidence and expensive surprises later. Use the right medium-specific tools and real hardware testing earlyand keep immersive UI consistent with your broader design system.

E-commerce prototyping is about revenue, not aesthetics. Test prototypes against conversion metrics across the full journey, especially mobile checkout, with realistic prices, inventory, and payment flows.