OpenAnalytics Semantic Decision Workbench
Build lean analytical pipelines that bind data, ontology, models, simulations, notebooks, evidence, and operational action. Semantics constrain execution before an LLM or robot gets the chance to improvise creatively.
EXECUTION ARCHITECTURE
LIVESemantic execution loop
COMPUTE CONNECTIONS
Execution boundary
START WITH A CAPABILITY
No 47-step wizard. Humanity survives.Lean analytic building blocks
OPEN OPERATIONAL ANALYTICS
From heterogeneous data to controlled action
OPENDATASQL • lake • search • graph • streams
→
OPENKNOWLEDGEontology • constraints • object identity
→
OPENANALYTICSpipelines • models • notebooks • simulations
→
OPENBUSreal-time events • replay • triggers
→
OPENTASKapproval • assignment • execution • audit
↔
OPENCOP / ROBOTSoperational picture • sensors • effects
Build a graph to visualize entities, links, provenance, and data lineage.
EXECUTION TARGETS
Jupyter servers
ANALYTIC ASSETS
Registered notebooks
jupyter nbconvert --execute or an approved runner, records the notebook path and environment, and reports results to the run API. Tokens remain in the compute environment, not in this registry.INTERCHANGEABLE ADAPTERS
Registered models
SEMANTICALLY GROUNDED LLM
EVIDENCE REQUIREDOpen WebUI assistant
The LLM receives a bounded semantic context. Unsupported evidence IDs are flagged before the result can become an OpenTask.
RUNPIPELINEKINDSTATUSREQUESTEDACTION