PC WEB SYSTEMS R&D

About Adaptive Intelligence Storage

PC Web Systems, LLC

PC Web Systems, LLC develops software, self-hosted server technology, artificial intelligence research projects and performance-focused computing solutions.

PC Web Systems, LLC

P.O. Box 4332
Arlington, VA 22204 USA

U.S. +1 (703) 946-9354
Mon-Sat 9AM-7PM EST (New York)

Contact PC Web Systems / AIS

AIS R&D Project

Project Lead: Sean L. Thompson

Contact the AIS R&D Project

Publisher and editorial responsibility

AIS News & Research is published by PC Web Systems, LLC. Articles identify the responsible human author, publication date and editorial category. Research updates, analysis and other editorial material follow the AIS Editorial Policy, including standards for sourcing, corrections, AI-assisted content and media attribution.

Adaptive Intelligence Storage (AIS)

AIS is a PC Web Systems research and development project focused on creating a fast, durable and intelligent storage layer for applications, websites and AI systems. It combines high-performance indexed storage with persistent state, adaptive caching and long-term memory structures that can survive application or model restarts.

  • Persistent AI memory: retains useful events, corrections, outcomes and retrieved context beyond a single AI session.
  • Application continuity: restores operational state after shutdown or restart instead of rebuilding it from scratch.
  • Performance acceleration: reduces repeated database, filesystem and computation work for frequently accessed information.
  • Model-independent design: keeps memory and application state outside any single AI model so the intelligence layer can evolve without losing accumulated history.
  • Clone4ever integration: provides structured storage for memories, associations, provenance, significance and later reinterpretation to support long-term digital continuity research.
  • Longitudinal learning: preserves errors, successful corrections and validated lessons so future AI systems can reuse what earlier systems learned.
  • Distributed future: provides a foundation for secure, provenance-aware state sharing among applications, people and heterogeneous AI systems.