Agentic AI systems designed for plants, OEM ecosystems, and supply chains. Engineered on Google Cloud — built for production scale.
Equipment failures during production hours cost manufacturers 5–20% of productive capacity. Scheduled maintenance is wasteful; reactive maintenance is catastrophic.
ML pipelines ingest real-time telemetry to detect anomaly patterns and predict failure windows 14–60 days ahead. AI agents autonomously schedule maintenance, check parts, and dispatch work orders without human intervention.
IoT sensors stream equipment telemetry via Pub/Sub in real-time
Dataflow processes and normalizes multi-sensor data streams
Vertex AI models detect anomalies and predict failure probability
AI agent schedules repair, orders parts, and updates ERP automatically
Manual QA inspects samples at limited speed, missing microscopic defects. Escaped defects drive costly recalls, warranty claims, and brand damage downstream.
Deep learning vision models inspect 100% of production output at full line speed. Agentic QA systems autonomously adjust upstream process parameters to prevent defect recurrence, reducing manual intervention by up to 80%.
Line cameras capture product images at full production speed
Vision API + custom Vertex AI models classify defect types in real-time
Defective units are flagged and automatically routed for rejection
Agentic system adjusts upstream process parameters to prevent recurrence
Quality data is trapped in QMS, MES, and ERP silos. Engineers spend most of their time stitching together reports — leaving organizations in permanent reactive firefighting mode.
A semi-autonomous AI quality framework on Vertex AI and Gemini that unifies QMS, MES, and IoT data. Specialized AI agents handle 90% of routine quality checks while human experts focus on strategic decisions.
BigQuery unifies data from QMS, MES, ERP, and IoT sensors
Gemini agents continuously monitor for cross-functional risk signals
Orchestrator AI connects insights across systems and escalates findings
Human experts review strategic decisions on a unified quality dashboard
OT, IT, and ERP systems operate in disconnected silos. Factory managers lack a unified real-time view of production, asset health, and supply chain performance.
A cloud-native Manufacturing Data Engine connecting PLCs, SCADA, MES, and ERP into a unified BigQuery platform. Real-time analytics and AI-powered insights surface production anomalies and optimization opportunities.
Manufacturing Connect bridges OT/IT data sources to the cloud
Dataflow pipelines normalize and harmonize multi-system data in real-time
BigQuery serves as the unified analytics layer across all factory data
Looker dashboards deliver real-time insights to operations and leadership
Physical prototyping for new products or process changes is expensive and slow. Manufacturers cannot rapidly iterate on designs or predict failure modes before committing production resources.
AI-powered virtual replicas of production lines on Google Cloud HPC. Run thousands of simultaneous simulations to test design variants, predict failure modes, and optimize throughput — before any physical change is made.
Physical asset data and CAD models are ingested to build digital replicas
HPC cluster runs thousands of parallel simulations simultaneously
AI models analyze simulation outcomes and identify optimal configurations
Validated designs pushed to production with zero physical rework risk
Warranty claims, supplier validations, and compliance workflows are bogged down by manual extraction and multi-step approvals — creating costly delays across OEM ecosystems.
Document AI pipelines automate extraction from warranty claims, invoices, and supplier documents. ML validation cross-checks against policy rules and ERP data, routing exceptions for human review while automating straight-through approvals.
Document AI extracts structured data from claims and supplier documents
ML validation checks against policy rules and ERP records
Clean claims are auto-approved; exceptions routed for human review
Outcomes feed back into models to continuously improve accuracy
Spare parts inventory is managed on gut feel. Critical components run out during emergencies; non-critical parts pile up — locking capital and delaying maintenance windows.
Time-series demand modeling on Vertex AI and BigQuery ML forecasts spare parts consumption based on equipment age, usage patterns, and predictive failure signals — dynamically optimizing reorder points across regions.
Consumption history, maintenance logs, and telemetry are ingested
BigQuery ML trains time-series models per SKU and equipment class
Vertex AI integrates predictive failure signals to anticipate demand spikes
Dynamic reorder recommendations sync to ERP procurement workflows
Whether starting with predictive maintenance or scaling a full agentic quality stack — our team is ready for enterprise and partner discussions.