AI Insights Intelligence
A conversational analytics layer for industrial operations: natural-language questions become trusted insights across SCADA, MES, historian, and warehouse data.
#Agentic Workflow
#Natural-Language Interface
SEE IT IN ACTION
What it does
A factory engineer types a plain-English question ("why was availability of Line 3 low on Tuesday?" and gets an answer in seconds: the SQL that ran, the result set, and an AI-generated summary of what the numbers mean. No query builder, no pre-built dashboard to navigate, no SQL knowledge required.
The system routes the question through Snowflake Cortex Analyst, which reads a semantic YAML model describing the factory's telemetry schema and generates the correct SQL. The query runs directly against live Ignition SCADA data stored in Snowflake. A second Cortex LLM call then summarizes the result into plain English — so the engineer reads a finding, not a table.
Why it matters
OEE data is rich and largely wasted. It sits in historians and data warehouses behind a SQL paywall that most floor engineers don't have. Pre-built dashboards answer the questions someone thought to ask six months ago, not the one the engineer has right now.
Natural language in, structured factory data out. It removes the analyst bottleneck entirely. The engineer with the question gets the answer directly. The insight surface is as wide as the data, not as wide as the dashboard.
Architecture
Ignition SCADA → Telemetry ingest → Snowflake + Semantic Model → Cortex Analyst (NL→SQL) → Snowpark (Cortex LLM) → Plain-English finding → Streamlit Chat UI
Key architectural features
Database and platform agnostic
The natural-language layer is decoupled from the underlying data source. While this demo uses Snowflake and Ignition, the same pattern can be adapted to other warehouses, historians, MES platforms, and structured industrial datasets with minimal changes.
Works with existing infrastructure
No dashboard rebuilds or shop-floor system changes are required. The solution sits on top of existing data pipelines, allowing organisations to unlock value from systems already in place.
Governed and explainable
Business logic is defined in a semantic model rather than hidden inside prompts. Generated SQL remains visible, making answers traceable, auditable, and aligned with approved operational definitions.
Scalable beyond OEE
The architecture is not limited to OEE analysis. The same approach can support downtime investigations, quality analysis, production reporting, energy monitoring, and other operational analytics use cases.
Rapid deployment
Because the solution uses existing telemetry and data platforms, implementation focuses on modelling business context rather than rebuilding infrastructure. Deployment is faster and integration effort is lower.
Turn natural-language questions into trusted insights instantly.