Context Graph
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.
Everything your agents need, connected as a graph. 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.
Want a technical overview of how it works end to end? Read the deep dive →
Drag nodes to explore. All connections are typed, directed edges that carry inherent meaning — governs, matches precedent, references, escalated from. Relationships are derived once in the canonical layer, ensuring a single version of the truth.
Three layers, one unified abstraction. The 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
MemoryRank: 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.
Click the central case to highlight its precedent cluster. Hover nodes for details. Shared neighbours are sized by how many precedent cases reference them — the higher the count, the stronger the signal.
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.
The system doesn't just accumulate knowledge — it learns which knowledge matters most for which situations.
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.
Cold-Start Your Corporate Memory
Interloom pre-processes your existing emails, notes, tickets, and cases to knowledge mine the corporate memory before a single new case arrives. The graph starts informed — no ramp-up period required.
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.
What the human sees, the agents see. What the agents can do, the humans can do. Knowledge is stored as structured articles that humans can read, edit, and approve — the same content is projected as token-efficient context for agents. Both sides operate on one shared truth, always in sync.
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%
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.”
Intelligence is abundant. Context is scarce. The companies that start capturing context now will have a lasting advantage that increases with every case processed.