Graph as Memory and Control Surface
We research how to make knowledge graphs the place where AI work is governed — not just stored.
Global thinking from local operations
We don't believe in a central brain orchestrating everything. Global coherence emerges from many nodes resolving locally, each following clear principles. Coordination happens through the shared graph, not through a master controller.
Node Resolution
The first-class operation that makes the graph a control surface.
What happens when you resolve a node
Plan
System determines what to search — domains, depth, budgets
Constrain
Type-gating restricts what can be extracted
Select
Appropriate capabilities are chosen for the task
Verify
Results checked against evidence
Log
Full provenance recorded at atomic level
What the platform coordinates
Language & Reasoning
LLMs, verifiers, fact-checkers, GraphRAG retrievers — coordinated through the graph.
Extraction
NER, Entity Resolution, deduplication, parsers, OCR/STT/vision capabilities.
Acquisition
Crawlers, API miners, ETL connectors, MCP/RPA tools — unified access.
Analytics & Policy
Graph analytics, anomaly detectors, validators, safety filters — governed execution.
Built-in guarantees
Deterministic
Planning on graph/policy snapshots produces predictable outcomes
Idempotent
Same inputs always produce the same result
Atomic
Commits with full provenance — all or nothing
Isolated
Context subgraphs prevent cross-contamination
Conflict-gated
Ontology-based arbitration resolves contradictions
Budget-controlled
Explicit quality-latency-cost trade-offs
Problems we address
Hallucinations & Entropy
Type-gating, evidence thresholds, and bounded search spaces reduce false information and control knowledge quality.
Opacity
Every decision emits an explanation subgraph. Signed provenance at atomic level enables inspection and reproducibility.
Unmeasured Autonomy
Hard and soft KPIs for extraction, linking, policy alignment, and efficiency enable quantitative tuning of AI behavior.
Agent Incoherence
Isolation via context subgraphs, conflict arbitration by ontology, and budgeted parallelism maintain stable multi-agent work.
Security & Governance
RBAC/ABAC enforcement, sandboxed execution, audit logs, and PII redaction ensure policy-compliant operation.
Cost Awareness
Explicit quality-latency-cost trade-offs surfaced at runtime enable predictable spending with transparent performance.
Want to learn more?
We share detailed technical documentation with research partners and collaborators.