Built an AI companion architecture with real internal needs — looking for first investor after publishing research paper

Reddit r/artificial 05/26/26, 09:20 PM Papers

ai-companion persistent-state memory-scoring realtime-telemetry infrastructure research-paper

Summary

The article presents a published architecture (research paper) for AI companions with persistent state, internal need variables, and memory scoring, seeking investment. The system, PHI // DRIFT, includes 18k+ lines of code and a real-time telemetry dashboard.

The problem with every AI product right now is that they're all wrappers. Same stateless LLM, different UI. The moment the context window closes, the AI forgets you existed. I built the infrastructure layer that fixes that. PHI // DRIFT gives an AI companion persistent state — seven internal need variables that drift between sessions, memory scored by what emotionally mattered not just what was semantically close, and a real-time telemetry dashboard showing the AI's internal state as it runs. This isn't a product yet. It's a published architecture with a research paper, 18k+ lines of working code, and 10 GitHub stars in the first 24 hours with zero marketing spend. The SaaS opportunity is clear: — Every company building AI companions needs this infrastructure layer — Enterprise AI that actually remembers context across sessions commands premium pricing — Security tooling that maintains reasoning state across bug bounty sessions is immediately monetizable I built this in 5 months on consumer hardware with $0. Imagine what happens with actual help Paper: [https://zenodo.org/records/20350249DM](https://zenodo.org/records/20350249DM)

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