Connect industrial protocols, route tag values, retain machine history in SQL, and analyze stored data for unusual patterns, all from one visual application.
Modbus TCP/RTU · MQTT · OPC UA · REST · TCP/IP · Serial · SQL
Bridged machines still can't answer the question that matters: is something about to go wrong? Raw values without history or analysis just move the blind spot from the machine to the dashboard.
A PLC speaks Modbus, your dashboard wants MQTT, your ERP wants a database row. Every mismatch becomes a custom middleware project instead of a reusable visual configuration your team can inspect, document, and reuse.
Live values disappear the moment they're read. Without retained history, there's no trend to compare against and no way to tell a normal fluctuation from an early warning.
Without anomaly detection watching the retained history, the first sign of a developing problem is usually the machine stopping, not a report that gave you time to act.
Protolinker interconnects industrial protocols (Modbus, MQTT, TCP/IP, OPC UA, REST APIs, serial devices, and SQL databases) and retains machine history. AI then analyzes the stored data for anomalies, trends, correlations, and possible contributing factors, and generates reports for engineering review.
Protolinker creates configurable data flows between industrial and software systems that normally speak different languages, reducing the need for custom middleware for every machine or protocol.
Data retention turns live machine readings into a historical record. Protolinker can store tag values in SQL databases so teams can review what happened, compare periods, and feed AI analysis.
The AI analysis layer evaluates historical tag values, identifies deviations from expected behavior, and surfaces events that deserve attention from maintenance and operations teams.
Scheduled AI-assisted reports turn trends, anomalies, correlations, and machine history into readable explanations for people who need to decide what to inspect, tune, or prioritize.
When data from multiple assets is analyzed together, teams can investigate relationships between temperature, vibration, cycle time, energy use, quality events, and equipment state.
Define tags, choose source and destination protocols, and link data points through a structured workflow that makes every route visible before data starts moving.
Protolinker can store machine and sensor values in SQL databases, creating the historical record needed for trend analysis, anomaly detection, reports, and operational review.
Add scripts to tags or links when values need transformation, validation, formatting, filtering, or custom business logic before they are forwarded to another system or stored for analysis.
Scan authorized local IPv4 networks, detect supported endpoints, review confidence and details, then create supported Modbus TCP, MQTT, OPC UA, or REST API connections from selected results.
Track protocol status changes, tag value changes, and errors with live viewing and retention. On supported plans, buffer tag-link writes during short disconnects and flush them after reconnection.
Save integration configurations as JSON, import existing setups, and run in CLI mode with a stored file so repeatable deployments can be started without rebuilding the setup from the UI.
Built with Qt and packaged for Windows, Linux, and macOS, Protolinker gives engineering teams a consistent workflow that can grow from pilot integrations to AI-assisted operational intelligence.
Four steps in one application. No developer needed to start; scripts are there when a case needs custom logic.
Point Protolinker at a machine, sensor, broker, API, or database. Visual setup, no custom code for every integration.
Store tag values in SQL so history builds up automatically: the dataset AI analysis and trend comparison actually need.
Isolation Forest anomaly detection evaluates retained history and flags deviations across machines, not just one sensor in isolation.
Scheduled AI reports summarize anomalies, trends, correlations, and possible contributing factors, emailed to the team for engineering review.
Interconnect PLCs, sensors, brokers, APIs, databases, and legacy machines across different protocols. Retain the operational history. Then apply AI reports, anomaly detection, and correlation analysis to support better decisions.
For teams that need more structure and reuse than custom middleware, without adopting a larger platform than the integration problem requires.
Protolinker is not intended to replace every SCADA, MES, historian, or enterprise automation platform.
Start free by moving machine data between protocols. Upgrade when you need more protocol types, automation, retention, anomaly detection, and AI-assisted reports.
For testing basic protocol interconnection with a small machine data flow.
For teams interconnecting more protocols and building a broader operational data layer.
For advanced protocol flows, data preparation, retention, and resilient production use.
For teams combining protocol interconnection, retained machine data, and AI-driven insight.
Protolinker has been validated with simulated equipment and services and is ready for controlled real-world pilots. Start with one machine, one useful data route, and agreed technical success criteria before expanding.
A quick guide to how Protolinker interconnects industrial protocols, retains machine data, detects anomalies, and uses AI reports to support maintenance, engineering, operations, and management.
Protolinker is built for teams that need connectivity and intelligence together: it interconnects protocols, retains machine data, and uses AI to turn that history into operational insight.
Protolinker interconnects industrial protocols and systems so data can move between machines, sensors, brokers, APIs, databases, dashboards, and legacy equipment. That data can then be retained, monitored, and analyzed with AI-assisted reports for trends, anomalies, correlations, and possible contributing factors, generated for engineering review.
The differential is the combination of protocol interconnection, data retention, and AI. When Protolinker connects heterogeneous machines and stores their history, AI can detect patterns, compare behavior, highlight anomalies, and generate clearer reports for engineering review than isolated telemetry would allow.
The current codebase includes support for Modbus TCP, Modbus RTU, MQTT, OPC UA, REST APIs, TCP/IP sockets, serial connections, and SQL databases, with client and server modes where implemented. These protocols can be interconnected to move data between systems and can also feed retained history for monitoring, storage, anomaly detection, and AI analysis.
No. Protolinker is designed to work with existing equipment and existing communication channels whenever those systems expose supported protocols. That makes legacy machine data useful for monitoring and AI-assisted analysis before larger infrastructure changes are required.
No. Developers can extend integrations with scripts, but the core workflow is visual: create protocol connections, define tags, link data points, monitor status, and schedule reports. This helps technical and operational teams work from the same machine data layer.
Protolinker can analyze retained SQL time-series data from machines and sensors, using machine learning such as Isolation Forest to identify values and behavior that differ from expected operational patterns.
AI-assisted reports analyze machine and sensor data for trends, anomalies, correlations, possible contributing factors, suggested investigation points, confidence, and limitations. They help teams turn retained historical telemetry into clearer operational insight.
Yes. On plans that include scripting, scripts can be attached to tags or links for formatting, validation, filtering, calculations, or custom logic. This helps prepare inconsistent machine values before they are forwarded, stored, or analyzed.
Yes, protocol discovery is available on supported plans. Protolinker can scan authorized local IPv4 networks, detect supported endpoints, show confidence and details, and create compatible protocol connections from selected results so those devices can become data sources.
Protolinker includes reconnect handling and, on supported plans, disconnected buffering for tag-link writes. Buffered writes can be held during short disconnects and flushed when the destination reconnects, reducing the risk of losing important values during temporary outages.
Bring one integration problem and review the protocols, systems, constraints, and a practical first validation scope.
Protolinker