Studying memory,
not just building it.
IRABrain is built on ongoing research into how AI systems should hold context over time. Some of it is settled engineering; a lot of it is still an open problem — here's what we're actually working on.
Four areas we keep coming back to.
Memory Architecture
How an AI system should store, retrieve, and forget — layered so short-term context and long-term history don't fight each other.
Behavioral Understanding
Recognizing patterns in someone's goals and habits over time, not just responding to the last message they sent.
Trust & Transparency
A memory system should be legible to the person it's remembering — clear about what's kept, and easy to correct or delete.
Ethical Engagement Design
Choosing patterns that support real progress over patterns that just maximize time spent in an app.
Problems nobody's fully solved yet.
We don't claim to have these figured out — they're the reason IRABrain keeps evolving instead of shipping once and stopping.
Staleness detection
How does a system know when something it remembers about you is no longer true, without you having to say so directly?
Procedural memory
Beyond facts and preferences — can an assistant learn how someone works, not just what they've told it?
Emotion-aware recall
Surfacing the right memory at the right moment matters as much as storing it correctly in the first place.
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