AiMe gives AI
continuity across time.
A cognitive operating layer that turns stateless models into systems that remember, track, and act over time.
Most AI resets. AiMe continues.
A continuous cognitive loop.
AiMe operates as a persistent loop — not a single request/response cycle. Each turn updates the user model and carries context forward.
Intent is classified deterministically before the LLM is invoked. The model is the last thing called, not the first.
Hippocampus RRF + Latent Episodes
calendar, scheduling, desktop
calendar, live feed
watch rules, pattern tracker
Live person-count, context snap
proactive candidates
It's a continuously maintained model of the user.
Context is not retrieved on demand — it's already active.
AiMe maintains a persistent, evolving model of the user — tracking identity, open concerns, relationships, behavioral patterns, and active goals. It holds these across sessions, not just within a single conversation.
The system doesn't look up facts about you when asked. It operates from a continuously maintained model of who you are — and everything relevant surfaces naturally from that context.
This is why swapping the underlying model doesn't break identity. The user model lives in AiMe — not in the model. Whichever cognitive engine responds, it responds from within the same persistent context.
Relevance by importance, not recency. Past context is scored by significance. High-significance episodes are injected before inference when the current turn connects to established portrait content.
A persistent model of the user. Six layers deep.
AiMe builds and maintains a structured, evolving representation of the user — persisted across sessions, updated after every turn. What they care about. What concerns remain open. What patterns repeat. What context is currently active.
Direct routing. Every specialist.
Intent is classified pre-LM by a governed specialist. The v4 spine runs three sequential specialist calls — each contract-bound to a single role, each with its own model selection. Four active providers with cloud-primary and local fallback on every call.
4 active providers: Gradient · Ollama · Gemini · xAI — cloud-primary with local fallback on every specialist. No single provider outage silences the system.
Six sovereign agents.
Six independent agents — each with its own state, lifecycle, and operational scope. They surface to AiMe. They do not narrate. They never block the turn path.
ProactiveLoop with ambient triggers and DMN scheduling. Multi-turn event staging. Conflict detection. Proactive schedule-candidate pipeline from email and chat. Explicit user directives auto-commit — suggestion paths stay approval-gated. Return and morning brief generation.
Significance-filtered inbox management. Multi-provider — Gmail OAuth and IMAP/SMTP. High-significance unread emails surface pre-LM as ★-marked entries. Behavioral feedback loop adjusts scores.
Vision-guided desktop automation. Plan → Confirm → Execute protocol. Live presence via Haar cascade (~200ms). Face-count delta triggers arrival and departure turns. 120s exit grace compensation. Fallback to snapshot DB when Thalamus unavailable.
Sovereign file agent with Windows-aware sandbox covering 11 threat vectors. Fingerprinted plan → confirm → execute with confirmation protocol. Atomic writes with rollback. Content-smuggling defense. 88 tests, 8 review rounds.
Multi-executor coding orchestration. Coordinates code writer, test runner, linter, and file manager executors. Structured task decomposition with review discipline built into the execution loop.
All standing interests — imprints, watch rules, and portrait concerns — are indexed as 768-dim BGE vectors in Qdrant. Every observation is checked against this index. Matches surface through a 6-check companion filter (quiet hours → activity gate → rate limit → per-intent cooldown → semantic dedup → consent grade) before AiMe is interrupted. Pattern tracker escalates recurring signals through 4 levels with significance boosts.
AiMe initiates turns — not just responds. 5-tier absence grading determines re-engagement tone. Return recognition fires a Gemini snapshot on arrival. Third-party presence detected via live bbox-count delta: count increase triggers an arrival turn, count decrease (while others present) triggers a departure acknowledgment. Fresh-boot detection prevents spurious greetings on system restart.
Same character. Different frame.
A swappable identity layer prepended to an immutable core operational contract. Switching personas changes voice and relationship stance — not the truth architecture or memory system beneath.
Personal companion mode. Relationship-first, continuity-aware. The Living Portrait of the user is the primary context frame. Warmth within the operational contract.
Operational governor mode. System Portrait active. Role-keyed authority bounds, incident stack, governance commitments. The model responds to role, not identity.
Simultaneous home and work frames. Same cognitive substrate, two portrait subjects. Context-aware blend: one frame when only one is active, both when both are relevant.
Production components
Live operational status of AiMe's core subsystems as of May 2026.
AiMe does not wait for prompts.
AiMe monitors relevance over time and surfaces information when it becomes meaningful. A concern mentioned days ago can reappear when conditions change. A pattern can be recognized without being explicitly asked. This is behavior driven by continuity — not input alone.
A system that remembers — without being asked.
Because it was still relevant.
Models are interchangeable. Continuity is not.
AiMe sits above any underlying model. What the model provides is inference. Everything else — identity, memory, behavior — lives in AiMe.
Continuity is preserved across model updates, provider switches, and capability upgrades — by design.
Request access to AiMe.
AiMe is currently in private deployment. Tell us about your use case and we'll follow up directly.
Continuity is the difference.