Context Graph | Interloom

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

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 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

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.

"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."