TENNI vs Personal AI

TENNI is an all-in-one platform designed for tennis, padel, and pickleball players, teams, and academies. It helps users find players at their skill level, organize events, and manage groups from casual friend circles to professional coaching teams. The app offers AI-powered video analysis that provides stroke-by-stroke feedback, enabling players to improve their technique without needing a coach present. Users can track their progress over time with dynamic skill ratings based on actual match results, making skill assessment transparent and up-to-date.

The platform supports running tournaments, seasons, and ongoing competitions with tools for sign-ups, brackets, and live results. TENNI also fosters community engagement through leaderboards, achievements, and challenges that motivate players to compete and improve. It integrates features for coaches and academies to manage rosters, schedule sessions, and monitor player development, scaling coaching impact beyond court hours.

Available on iOS and Android, TENNI replaces fragmented group chats and spreadsheets with a centralized hub for racket sports enthusiasts. It supports multiple sports in one app, adapting to users' preferences whether they focus on tennis, padel, or pickleball. The platform emphasizes ease of use, community building, and data-driven improvement, making it a comprehensive companion for racket sport lifestyles.

Personal AI builds memory infrastructure that gives AI a persistent identity instead of resetting every session. Its Memory Core encodes, stabilizes, stores, retrieves, and updates experiences so an assistant remembers what it has lived, not just documents you upload. The stack spans My AI for end users, Persona Studio for builders, and carrier-grade Memory Core deployments on operator networks.

Most AI assistants treat context as static files you attach before each chat. Personal AI separates memory (accumulated experiences with temporal structure) from context (static uploads) and lets identity emerge over time. Carrier benchmarks on Comcast's network cite 15ms time to first token, $0.02 per million tokens, and sub-500ms end-to-end voice versus cloud LLM baselines.

Telecom carriers, enterprise AI teams, and individual users who want a long-running assistant use Personal AI. Carriers deploy MODEL-4 on their own networks under custom agreements. Builders use Persona Studio to configure personas without code. Consumers get My AI, an assistant that remembers conversations and learns preferences over time.