Industry ยท Manufacturing & industrial IoTControl factory data at the source.
Your MES knows about defects after they ship. Your predictive maintenance predicts yesterday. Run quality ML on the line. Catch problems in process.
Factory data needs upstream control.
Control data at the source. Send only what matters downstream. Prevent downtime faster.
What Expanso delivers
Make manufacturing data AI-ready
Factory floor data needs alignment, qualification, and governance for AI-powered production.
Add semantic meaning to sensor data. Convert raw PLC signals to structured, analyzable formats.
Enforce data quality at the production line. Route anomalous readings for review automatically.
Assess the complete pipeline and operating environment against the applicable quality and regulatory requirements.
Enhance the platforms you already use.
Snowflake for analytics. Databricks for AI. Splunk for security. Datadog for observability. Expanso sits upstream, sending clean, governed data to all of them.
Explore integrations
Kafka
PostgreSQL
Snowflake
NATS
ClickHouse
Redis
MQTT
Splunk
Oracle
MySQL
Redpanda
AWS
Azure
Google Cloud
OpenSearch
Parquet
Starburst
Databricks
OpenAI
Pinecone
Qdrant
Confluent
OPC UA
Ollama
SQLiteProcessing terabytes of sensor data in centralized platforms?
Expanso filters, transforms, and governs data at the source, before it reaches your data centers. Accelerate production decisions while preventing downtime.
Powering the next generation of smart manufacturing
Real-time defect detection at the source
Unified operations without centralization delays
Process logistics data locally with governance
Configure controls and validate them across facilities
Break down silos with governed data sharing
Process sensor data at the source with governance
Real-time adjustments with clean data
Monitor and optimize power consumption in real-time
Frequently asked questions
Expanso can use the documented MQTT input and IoT aggregation patterns to process telemetry on customer-controlled nodes and send configured outputs downstream. Any volume or cost change must be measured for the selected data, rules, and destination.
Use documented inputs such as MQTT and validate the intended pipeline against each PLC, SCADA system, gateway, and downstream destination. Network topology and data-movement controls depend on the customer environment and must be tested there.
Expanso can process production-line records on connected nodes and route results according to configured rules. Use documented Ollama API processor components where applicable; do not infer a native model runtime. Measure latency and alert behavior on the intended hardware and pipeline.
Teams can configure processing and metadata for their manufacturing workflows, but architecture alone does not guarantee compliance or certification. Validate the complete pipeline, surrounding controls, and retained evidence against each applicable requirement.
Deployment timing depends on node count, factory environments, connectivity, approvals, and acceptance testing. Validate configuration rollout and observed state at each connected location before relying on fleet-wide consistency.
Predictive maintenance predicting yesterday?
If your quality models run on batch data, or your production line learns about defects after they ship, we should talk.
