# Codified operational context for every decision your team and agents make.

Data is abundant. Context is scarce. The Context Graph transforms raw workflows into a semantic memory layer that grounds agents and experts in verified precedent, structured relationships, and the collective intelligence of your operations.

## Context Graph for Better Decisions
When an agent acts, it draws from layers of context — from the immediate thread to the full organizational workspace. Closer rings carry higher relevance. The result is a precisely scoped, token-efficient context window for every invocation.

### External Systems
- **Agent CLI**: Salesforce, SAP, Jira, ServiceNow, Slack, ...
- **Space Agent CLI**: Files, E-Mails, Tables, Procedures, Notes, ...
- **Relevant Agent CLI**: Similar cases, policies, articles, citations, ...
- **Trace Agent CLI**: Actions, inputs, outputs, decisions, ...
- **Case Agent CLI**: Thread, actors, artifacts, activities, ...

The Context Graph resolves operational entities into first-class objects with stable identities and typed semantic relationships. Not rows in a database — nodes in a living knowledge structure.

### Three Layers, One Unified Abstraction
- **Memory Layer**: Decouples the meaning of an event from its underlying storage. Raw data stays where it lives. The graph resolves it into context your agents can reason over.
- **Projection**: Human dashboards · Agent context windows
- **Canonical**: Entity resolution · Semantic relationships
- **Source**: Databases · Files · Email systems

## Memory Rank
Precedent as outcome signal. Back-office work repeats in patterns. Interloom captures the relationships behind successful resolutions, clusters new cases to matching precedent, and reranks the artifacts, actions, and know-how that most often led to strong outcomes.

### Property Endorsement
- **Broker Chat — Urgent Similar Cases**: 
  - IP Infringement — Alpha Inc: 94%
  - Breach of Contract — Beta LL: 91%
  - Partnership Dispute — Gamma: 88%
  - Licensing Agreement — Delta: 85%
  - Employment Contract Dispute: 82%
  - Patent Violation — Omega Sys: 78%
  - Service Agreement Breach: 76%
  - Non-Compete Clause Dispute: 74%

### Reinforced by Completed Cases
Each case outcome strengthens the graph connections it traversed. Over months, heavily-trodden paths become high-confidence precedent. Rarely-used paths signal edge cases worth human review.

## Precedent Discovery via Triangulation
When a new case arrives, the graph automatically triangulates to find matching guidelines and precedent cases. Matches are ranked by request type, domain category, and recency — not keyword overlap.

### Grounding That Agents Can Cite
When an agent makes a decision, it references specific objects and relationships in the Context Graph. Every output links back to the knowledge and precedent that informed it.

## Cited Outputs
Agent actions include citations to the specific knowledge objects, case precedent, and procedure steps that informed each decision. Reviewers verify reasoning in seconds.

### Single Source of Truth
The canonical layer ensures every agent, dashboard, and workflow draws from the same resolved graph. No conflicting copies, no stale caches, no drift between teams.

### Enterprise Governance
Granular permissions over specific nodes and relationships. Control who can read, write, and approve changes to the graph — down to individual entity types and connections.

## Structured Articles
Each knowledge entry is a readable article with sections, references, and metadata. No opaque vector stores. Humans audit, edit, and approve content directly.

### Version History
Every edit is tracked. See how knowledge evolved, who changed it, and why. Roll back to any previous version. Full audit trail by default.

### Portable and Open
Export your full knowledge graph at any time in standard formats. Import existing documentation, process manuals, and knowledge bases. No lock-in.

## Proof of Concept
### Broker-Underwriter Chat Codification
**Challenge**: High-velocity broker chats were unstructured and undocumented. 50% of customer support interactions lacked traceable resolution, with no systematic way to extract intent, trade context, or apply institutional knowledge at scale.

**Outcome**: AI agents extracted broker, underwriter, intent, sentiment, trade type, and solution from each interaction — then triangulated against guidelines and historical precedent to codify every chat into the context graph.

- Undocumented support requests, down from 50%
- >95% Accuracy on recommendations to knowledge teams

> "The agents didn’t have to guess — they were fueled by the collective, codified memory of the business. A rules engine is as good on day one thousand as it is on day one. A context-driven system is measurably better."