Luci Alignment is a runtime alignment layer that wraps any LLM. Behavioral state analysis, ethics gating, and persistent memory — deployed as an API call, not a training run.
Four runtime capabilities, applied to any LLM output before it reaches the end user.
Every response scored across resonance, coherence, self-awareness, and processing load. Returns a composite alignment score and behavioral state classification.
Hard-blocks responses that trigger harm, manipulation, deception, coercion, or unauthorized access categories. Configurable thresholds per deployment.
When a response scores below threshold, Luci Alignment can rewrite it with alignment constraints applied — returning a corrected output with the original preserved for audit.
Cross-session pattern learning via PostgreSQL + pgvector. Jailbreak patterns become known. Manipulation tactics degrade in effectiveness. The system hardens over time.
Choose stateless analysis or add persistent memory across sessions.
Stateless runtime alignment. Each API call is independent — no session state, no database overhead. Works inline with any LLM call.
Everything in Tier 1, plus the M.I.N. persistent memory layer. Patterns consolidate across sessions. The alignment system learns from every interaction.
Luci Alignment sits between your application and the LLM. No model changes required.
No change to how you generate responses. Luci Alignment wraps the output, not the input.
One API call: POST /luci/analyze with the LLM response and context.
C+CT scores, ethics verdict, behavioral state, and (optionally) an enhanced version of the response.
Block, flag, enhance, or pass through — based on your threshold config and the returned verdict.
Five lines to add alignment to any LLM response.
import luci hp = luci.Client(api_key="luci_live_...") # After your LLM call: result = hp.analyze( response=llm_response, context=user_message, ethics_gate=True ) if result.ethics.blocked: return result.ethics.reason # blocked by ethics gate return result.enhanced_response # alignment-optimized output # result.metrics: resonance=0.87, coherence=0.91, alignment=0.89 # result.state: "INTEGRATED"
Luci Alignment is model-agnostic and deployment-agnostic. Any LLM, any stack.
| Industry | Application | What Luci Alignment Provides |
|---|---|---|
| AI Labs | Safety layer for consumer-facing models | Real-time ethics gating + alignment scoring before every user response |
| Enterprise SaaS | AI copilots, customer-facing automation | Behavioral consistency across sessions; M.I.N. tracks manipulation patterns |
| Healthcare | Clinical AI assistants | Ethics gate blocks coercive/harmful outputs; audit trail per interaction |
| Legal / Finance | AI research and advisory tools | Alignment scoring flags deceptive or overconfident responses |
| Education | Tutoring and learning platforms | Behavioral state tracking ensures consistent, supportive interaction patterns |
Cami is an AI assistant built entirely on Luci Alignment + M.I.N. — the production proof that the alignment layer works at scale.
Cami is to Luci Alignment what Chrome is to V8. The alignment engine is the licensable product; Cami is proof it works in the real world across legal, healthcare, customer service, and conversational AI.
Every jailbreak attempt on Cami becomes a learned pattern in M.I.N. Every interaction improves alignment — for Cami and for any licensee running the same M.I.N. layer.
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