Engagement
AI at scale, results at scale
The semantic model, the pipelines, and the reporting layer that makes both usable.
The semantic model
What each metric means, who owns it, how it is calculated, and where the exceptions live. Scoped by value stream so a disagreement routes to someone who can settle it.
The pipelines
Designed against the model, not against a vendor’s reference architecture. Your ERP, your MES, your quality system, landing where the model says they belong.
The reporting layer
The part operators actually open. Roughly 80% of what we deliver is conventional business intelligence, and it earns its keep on day one.
Target Architecture
What we build.
Teal is the namespace. Navy is where your definitions get settled.
Plant floor·PLCs, SCADA, historian
Business systems·ERP, PLM, MES, QMS
MQTT broker·HiveMQ or EMQX
Edge gateway·filter, buffer, forward
Unified namespace·enterprise / site / area / line / cell / tag
Managed streaming ingest
Kafka, only if replay is needed
Snowflake
Databricks
Semantic layer·reconciles ERP and shop floor definitions
OEE dashboards
Predictive models
Board reporting
Nothing above the semantic layer is worth building until the layer itself is settled. That is why Kafka is dashed. You buy it when you need replay, not because a reference architecture said so.
Standard work before AI work
Start with the definitions. The audit takes twenty minutes and tells you whether a platform is the right next spend.