What RAMSES Is
RAMSES is not a wrapper over GPT or Claude. It's a specific architecture for capturing and retrieving reasoning—the "why" behind decisions, not just the "what."
Most AI memory systems store text. RAMSES stores reasoning—extracted, structured, and connected in a graph that preserves context, alternatives considered, trade-offs accepted, and outcomes observed.
This makes it possible to ask "why was this decision made?" and get complete answers, not keyword matches or summaries.
How It Works
Graph with reasoning nodes
Each node is not raw text—it's an LLM-extracted reasoning unit. A decision, a chain of thought, a context. Nodes connect: decision A led to decision B, which caused problem C. Or: process X, deviation Y, error Z. The graph preserves the entire thread.
Atomic Semantic Units (ASUs)
From each reasoning piece, RAMSES uses LLMs to extract base concepts—"pricing decision," "vendor evaluation," "customer complaint." These enable granular semantic retrieval, not full-text matching. When someone asks "why are customers unhappy?", RAMSES finds not only "customer satisfaction" but also "churn," "complaints," "support tickets"—all semantically linked via ASUs.
Metadata for intelligent retrieval
Context: when, who, about what. Connections: what decisions followed, what preceded. Impact: what happened after.
Many metadata fields are predefined (financial, technical, HR, legal), some are LLM-suggested for approval. When the LLM notices a new pattern—"remote work policy decisions"—it proposes a metadata category. Admin approves or rejects.
E-E-A-T ranking
Results are ranked by:
• Experience: how often this pattern appears
• Expertise: who made the decision (CFO > intern for financial matters)
• Authority: official decision vs brainstorming session
• Trust: did the decision work—outcome tracking
When retrieval finds 10 reasoning nodes on "pricing strategy," E-E-A-T decides which appear first. The CFO's decision that increased revenue outranks a brainstorming idea that led nowhere.
Why this matters: This is not keyword search. You ask "why don't we offer monthly billing?"—keyword search finds docs with "monthly" and "billing." RAMSES returns the complete reasoning behind that decision, ranked by E-E-A-T, with all connections (what led to it, what followed, what alternatives were considered).
Applications
ROM - RAMSES Organizational Memory
ROM applies RAMSES to organizational knowledge capture. It connects to meetings, email, chat, ERP, CRM—ingesting each interaction with full context and storing reasoning, not transcripts.
When someone leaves, their reasoning stays attached to the organizational role (CFO, CTO), not the person. New people in that role immediately see the complete decision history—every alternative considered, every trade-off, every outcome.
Living Knowledge Ecosystem
RAMSES as personal memory system—processing conversations into semantic units connected through temporal and semantic relationships. Features sleep cycles for memory consolidation, functioning like biological memory patterns.
In development.
RAMSES is designed as infrastructure—adaptable to different domains where reasoning capture and retrieval matter. The core architecture remains the same; the integrations and metadata schemas adapt to context.
LLM-Agnostic by Design
RAMSES uses the best reasoning models available today—currently Claude Sonnet 4.5 and GPT-4 for reasoning extraction. When better models emerge, we integrate them.
The differentiation isn't "better AI"—everyone has access to the same LLMs. The differentiation is the architecture: how reasoning is extracted, structured, connected, and retrieved. That's defensible. LLM capabilities are commoditized.
Philosophy
Organizations, individuals, and systems make thousands of decisions. Most of that reasoning disappears within hours—captured poorly, stored nowhere, retrieved never.
RAMSES makes reasoning persistent and retrievable. Not as surveillance. Not as control. As infrastructure for coherence—the ability to understand why decisions were made and learn from them over time.
We don't promise transformation. We provide infrastructure. What you do with transparent reasoning is your choice.
Get In Touch
If you're building something that requires reasoning capture and retrieval at scale—or if you see applications we haven't considered—let's talk.
Email: admin@clotier.eu
Technical conversations, partnership inquiries, or applications of RAMSES architecture.