How REMI Thinks
A governed intelligence system for real estate investment teams.
REMI is designed to connect analysis and execution. It builds contextual memory, improves recommendations from your own decisions, and keeps human governance in control at each critical step.
Context Graph
Connects every deal, assumption, decision, and outcome so teams work from shared context instead of disconnected files.
Recursive Learning
Every approval, rejection, and override improves future recommendations using your firm’s own operating patterns.
Human Governance
Learning loops are controlled by your team’s decisions, permissions, and approval chains.
Proof Without Customers
Verifiable trust signals before case studies exist.
- Replay mode against historical deals and reports
- Source-linked outputs with citation and lineage
- Decision diffs between model draft and final human-approved version
- Shadow mode before any live workflow execution
Moat Demo Concept
Compounding Intelligence Loop
A future Remotion sequence can show how one approval decision cascades into better screening, tighter monitoring, and cleaner LP reporting over time.
Narrative arc: first run baseline to human override to improved subsequent decisions.
Hidden Alpha
Subtle signals that position REMI as a category-defining operating layer.
Institutional memory compounding across teams and vintages
Execution intelligence, not just document generation
Governed automation that improves with committee behavior