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.
Watch REMI run a real decision.
Every recommendation links to a document, an email, or a precedent your firm created years ago. General AI sees a $50/sq ft lease. REMI sees the reasoning behind every decision ever made about it.
Explainable paths
Every recommendation is backed by a link to a real document, email, or prior decision.
No catastrophic forgetting
When the senior partner who ran that 2019 deal left, their logic stayed in the graph as a durable asset.
Compounding value
Every approval or correction trains your World Model — not just completes a task.
Not a database. A living map of your firm.
The Context Graph is REMI's core data structure. It connects every deal, asset, fund, person, document, and decision into a single queryable knowledge layer — so AI agents can reason at the firm level, not just the spreadsheet level.
Entity Resolution
Every asset, sponsor, tenant, market, and covenant is normalized and linked — across thousands of documents, emails, and data sources.
Relationship Mapping
A deal is connected to an asset, which sits in a portfolio, inside a fund. The graph preserves these relationships so agents can traverse them instantly.
Decision Memory
Every IC approval, rejection, and override is recorded in the graph. Over time, the graph learns your firm's thesis, risk appetite, and preferences.
Temporal Awareness
The graph tracks how assumptions, valuations, and market conditions change over time — enabling comparisons across vintages and cycles.
Hybrid Retrieval
Combines graph traversal (structured relationships) with vector search (semantic similarity) so agents find the right context through multiple paths.
Firm Ontology
Your buy-box rules, IC rubrics, LP reporting preferences, and formatting standards are encoded as constraints that agents respect automatically.
The Context Graph is our moat. Competitors can copy features — they can't copy your firm's accumulated intelligence.
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
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.