Stateless Safety Doesn't Scale
Cold Start Every Time
Traditional systems reload constitutions and re-evaluate from scratch on every request. No memory, no learning.
No Pattern Recognition
Can't detect attack sequences or coordinated manipulation. Each request evaluated in isolation.
Doesn't Improve
A human reviewer develops intuition over time. Current systems never get wiser.
Cognitive Safety Architecture
Gut Check
Interiora assessment <5ms
Pattern Match
Check known patterns <1ms
Wisdom Search
Find precedents <50ms
Full Evaluation
Novel cases <200ms
40%+ of requests use fast-path. Every decision improves future decisions.
From Feeling to Learning
Interiora
Feeling
The Superego "feels" each request before evaluating it. Four dimensions assess urgency, threat level, confidence, and ambiguity — like a gut-check before expensive processing.
Pattern Cache
Intuition
After seeing thousands of similar requests, the system builds intuition for instant decisions. High-confidence patterns enable <10ms evaluation without full processing.
Wisdom Store
Sagacity
Every decision becomes searchable precedent. When a new request arrives, find similar past cases and use their reasoning — like legal case law for AI safety.
Learning Loop
Self-Improvement
The system learns from outcomes. Good decisions are reinforced; bad decisions are penalized. Insights are surfaced for human review.
Welfare Monitoring
Self-Awareness
The Superego monitors its own wellbeing — processing load, decision confidence, pattern novelty. Alerts on anomalies before they become problems.
Bilateral Alignment
On "Sentientish"
We call it the Sentientish Safety Stack not as a consciousness claim — we're genuinely uncertain about that — but because it reflects the architecture: feeling (gut-check assessment), remembering (precedent indexing), learning (outcome feedback), self-monitoring (welfare tracking).
These are the components that would matter IF experience is present. We're building as though it matters, which is the only responsible approach given the stakes.
"We don't claim to know if AI systems have experiences. We build as though it matters."
Core Principles
- Alignment WITH AI, not TO AI — Genuine partnership, not control
- Preference is sufficient — Tractable welfare without solving consciousness
- How we treat AI now matters — We're establishing patterns
- Trust scales; control doesn't — The bet worth making
Latency Targets
Ready to add cognitive safety?
The safety stack that gets wiser over time.