Manufacturing
Industrial IoT, AI quality control, OEE analytics, and ERP integration — engineered with the OT/IT architecture, edge computing layer, and zero-disruption deployment approach that manufacturing operations require.
What We Offer
Real-time MES solutions that track production orders, machine status, quality data, and labor across your shop floor in a single unified platform.
IoT sensor integration and ML models that predict equipment failures before they happen, reducing unplanned downtime and maintenance costs.
Computer vision and machine learning systems that inspect products at line speed, detecting defects with greater accuracy than manual inspection.
End-to-end supply chain visibility with demand forecasting, supplier management, and inventory optimization to reduce costs and prevent stockouts.
Connect machines, sensors, and systems across your factory floor with IIoT platforms that collect, process, and visualize operational data in real time.
Overall Equipment Effectiveness dashboards and production analytics that identify bottlenecks, track KPIs, and drive continuous improvement.
Integrate shop floor systems with SAP, Oracle, and Microsoft Dynamics, or modernize legacy ERP systems to cloud-native architectures.
Virtual replicas of production lines and equipment that enable simulation, optimization, and remote monitoring without disrupting live operations.
Why SolveJet
Most industrial software projects fail not because the analytics platform was wrong, but because the edge layer, protocol handling, and network segmentation were an afterthought. We start there.
Connecting PLCs, SCADA, and CNC machines to cloud analytics requires an edge computing layer that handles protocol translation, local buffering, and secure upward data flow. We design this layer first — not as a connector bolted onto an IT platform.
We follow IEC 62443 and NIST frameworks for industrial control systems — network segmentation between OT and IT zones, encrypted communications, and anomaly monitoring — because a compromised shop floor network is an operational risk, not just an IT incident.
Manufacturing systems can't go down for a cutover. We deploy pilot-first on non-critical lines, validate with real production data, then roll out to full operations — so new systems are proven before they touch your highest-value assets.
As a Google Cloud Partner, we build Pub/Sub and BigQuery pipelines that ingest high-frequency sensor data, run ML inference at the edge, and surface OEE analytics to operations teams in near real-time — without the infrastructure overhead of on-premise historian servers.
Proven Outcomes
These are the benchmarks well-built industrial platforms consistently move — and what we design toward on every engagement.
Trusted by
What We Build
These illustrate the systems we design and engineer — the technical approach, the integration patterns, and the operational outcomes they're built to deliver.
An edge-first predictive maintenance system — vibration, temperature, and pressure sensors connected via OPC-UA and MQTT to an edge computing layer, with ML failure prediction models running locally and aggregated health dashboards served from GCP. Automated work order creation in the CMMS when thresholds are breached.
Explore this solution
A computer vision inspection system with high-speed cameras, edge AI inference via TensorFlow Lite or Vertex AI Edge, real-time rejection and diversion control, defect classification at 99%+ accuracy, and a quality trend dashboard with SPC charts — integrated into the production line without stopping for rework.
Explore this solutionA cloud-native MES on GCP with bi-directional SAP/Oracle ERP integration — production order tracking from release to completion, live OEE calculation per machine, electronic work instructions, quality checkpoints, and labor tracking. Deployed pilot-first on non-critical lines before full rollout.
Explore this solutionA BigQuery-backed supply chain platform with ML demand forecasting from production and sales signals, a supplier collaboration portal, real-time inventory visibility across locations, automated reorder point calculation, and supplier performance scorecards — integrated with the ERP for purchase order automation.
Explore this solutionOur Process
A structured engagement model built for the operational complexity and zero-disruption requirements of manufacturing businesses.
We audit your production processes, existing systems (SCADA, ERP, MES), and connectivity landscape to define the right solution scope.
Our architects design the integration blueprint — edge computing layers, data pipelines, security zones, and cloud connectivity plan.
Two-week sprints with pilot deployments on non-critical lines first. Validate with real production data before rolling out to full operations.
Factory acceptance testing, cybersecurity assessment, and performance validation under real production conditions.
Phased rollout across all lines and facilities. Operator training, maintenance team handover, and 90-day hypercare support.
Use Cases
From predictive maintenance to AI quality control, see the specific problems we solve for manufacturing businesses.
Predictive Maintenance
Connect IoT sensors to ML models that analyze machine health data in real time, predicting failures weeks in advance so maintenance can be planned during scheduled downtime.
Technology Stack
We build on proven, enterprise-grade technology — and integrate with the industrial systems you already run.
FAQ
Everything you need to know about modernizing your manufacturing operations with SolveJet.
We build Manufacturing Execution Systems (MES), Industrial IoT platforms, predictive maintenance systems, AI quality control solutions, digital twins, OEE analytics platforms, and supply chain optimization tools. We also integrate with existing ERP systems (SAP, Oracle, Microsoft Dynamics) and SCADA/PLC systems.
AI-powered computer vision systems inspect products at line speed — detecting surface defects, dimensional variations, and assembly errors with 99%+ accuracy. This replaces or augments manual inspection, reduces defect escape rates, and provides real-time quality data for process improvement.
We connect IoT sensors to ML models that analyze vibration, temperature, pressure, and electrical signatures to predict equipment failures 2–4 weeks in advance. This allows maintenance to be scheduled during planned downtime, reducing unplanned stoppages by 40–60%.
Yes. We use industrial protocols including OPC-UA, MQTT, Modbus, and PROFINET to connect with existing SCADA systems, PLCs, and CNC machines. We build edge computing layers that process data locally before sending aggregated insights to cloud platforms.
We follow IEC 62443 and NIST cybersecurity frameworks for industrial control systems. This includes network segmentation between OT and IT networks, encrypted communications, role-based access controls, and security monitoring for anomalous behavior on the shop floor network.
"They don't force us to go their way; instead, they follow our way of thinking."
★★★★★Marek StrzelczykHead of New Products & IT, GS1 Polska
