What We Do
Our Practice Areas
We deliver focused, senior-led engagements across three practice areas. Our work is structured to produce durable outcomes, architecture your team understands, pipelines your team can maintain, and dashboards that inform decisions that matter.
Industrial Data Infrastructure
The industrial data stack has changed. What used to require a dedicated on-premises historian cluster and a team of database administrators can now be built on cloud-native infrastructure that scales with your operation and costs a fraction of legacy alternatives. But translating that promise into a production system for an industrial environment requires specific expertise.
We architect the data platforms that serve as the foundation for everything above — ingestion layers, lakehouse designs, streaming pipelines, and the data contracts that make downstream analytics reliable. We build for the engineering team that will maintain it, not for the demo.
Typical Engagement Scope
- →Industrial data lakehouse design and implementation (Delta Lake, Iceberg)
- →Streaming pipeline architecture (Kafka, Spark Streaming, Flink)
- →Cloud infrastructure design (Azure, AWS) for industrial workloads
- →Data quality framework and observability layer
- →dbt modeling and transformation layer development
Delta Lake · Apache Iceberg · Kafka · Spark · dbt · Azure · AWS · Databricks
Industrial Analytics & BI
Operational data has no value until it reaches the people who can act on it. Most industrial BI projects fail not because the data is unavailable, but because the dashboards are built for data engineers rather than operations managers — and because the KPI definitions were never aligned to what actually drives plant performance.
We build the analytics layer that bridges that gap. Our work includes KPI framework design, semantic data modeling, dashboard development, and the training and documentation that ensure your team can own and extend what we build. We work in the tools your organization has already invested in, and we design for operational use — not for quarterly reviews.
Typical Engagement Scope
- →KPI framework design and operational metrics alignment
- →Semantic layer and data model development (dbt, Databricks, Snowflake)
- →Dashboard development (Power BI, Grafana, Tableau)
- →OEE tracking, downtime analysis, and production yield reporting
- →Real-time alerting and threshold-based notification pipelines
Power BI · Grafana · dbt · Databricks · Snowflake · Python · SQL
Industrial Intelligence
The next layer of value in industrial operations is not more dashboards — it is systems that learn from your data, surface anomalies before they become failures, and take action on your behalf. Most organizations have the data. What they lack is the architecture to put machine intelligence to work against it.
We design and implement the ML and agentic systems that operate on your industrial data infrastructure — anomaly detection models trained on your process signals, predictive maintenance pipelines tuned to your equipment, and autonomous agents that monitor, escalate, and respond without waiting for a human to notice. We build these systems to be explainable, maintainable, and grounded in your operational reality.
Typical Engagement Scope
- →Anomaly detection model development on time-series process data
- →Predictive maintenance pipeline design and deployment
- →Agentic monitoring systems with configurable escalation logic
- →ML model operationalization and retraining pipelines
- →Edge inference deployment for latency-sensitive environments
Python · scikit-learn · PyTorch · MLflow · FastAPI · Kafka · Edge inference
Not sure which practice area fits your situation?
Talk to Our Team →If you're still solving production problems after the they happen, it's time to change that.
Most engagements start with a 60-minute discovery session. This is an honest conversation about where your operation is today and what digital transformation would actually look like for your team.
Schedule a Discovery Session →Or reach us directly at info@signaliiq.com