The substrate everything else stands on. We benchmark your data estate, then architect and operate the platforms — lakes, streams, lakehouses, feature stores, MLOps — that make machine learning, generative AI, and advanced analytics deliverable at industrial scale and reliability.
Models, dashboards, and copilots are downstream of data infrastructure. If the data is late, dirty, ambiguous, or unobservable, the most sophisticated AI on top will fail. The Pentaaxis big-data practice treats the underlying platform as the primary product — engineered for the scale, latency, lineage, and governance that industrial operations require.
We design for the realities of industry: hybrid on-prem and cloud, OT/IT bridging, time-series at line frequency, ten-year retention for compliance, lineage to satisfy auditors, and recovery objectives measured against unplanned downtime cost. Our reference architectures pair lakehouse fabric (Delta Lake, Iceberg) with proven streaming (Kafka, Flink) and orchestration (Airflow, Dagster).
A working catalogue of the systems, models, and platforms our engineers ship within this practice — selected for industrial reliability, observability, and scale.
Lakehouse architectures unifying OT (sensors, MES) and IT (ERP, CRM) data with full lineage and time-travel.
Kafka- and Flink-based real-time pipelines for telemetry, event capture, and stream analytics at line frequency.
Centralized feature platforms serving consistent features to training and production with low latency and full lineage.
End-to-end ML lifecycle: experiment tracking, model registry, deployment, monitoring, automated retraining.
Self-serve analytics platforms with semantic layers, governed datasets, embedded BI for line, plant, and executive views.
Catalogues, lineage, access policies, masking, and audit trails meeting GxP, ISO 27001, and SOC 2 requirements.
Representative deployments across our industrial client base. Each grounded in production engineering — not concept slides.
Unified data platform ingesting MES, SCADA, historian, ERP, and quality systems across a global manufacturing footprint with single semantic layer.
Kafka + Flink pipelines processing millions of sensor events per second with windowed aggregations, anomaly scoring, and downstream model invocation.
Architectures that respect Purdue Model boundaries while enabling secure, governed data flow from PLC/SCADA to cloud analytics.
Modernize legacy historians with cloud-native time-series stores while preserving analytical continuity for control engineers.
End-to-end traceability platform linking raw materials, in-process measurements, finished goods, and field returns.
Cross-tier data sharing with privacy-preserving analytics, allowing trading partners to share signals without exposing competitive data.
End-to-end MLOps stack: feature store, registry, deployment, drift detection, automated retraining, governance for hundreds of models.
Semantic layer + dbt + BI tools allowing plant managers to ask new questions of certified data without waiting on central analytics teams.
Tools, frameworks, and platforms our engineers use day-to-day in this practice.
Every Pentaaxis engagement starts with a structured benchmarking conversation — no obligation, a senior engineer in the room, and a calibrated view of where AI moves the needle for your organization.