Edmund offers an AI-powered platform that consolidates electrical schematics, PLC code, IoT telemetry, and maintenance records into a searchable knowledge graph. The system’s assistant interprets error codes, maps root causes across data sources, and provides step‑by‑step repair instructions, reducing equipment downtime and preserving expertise in manufacturing plants.
Funding
€3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
CFFOLVTVUFounders
Product
Problem
Manufacturing plants often experience unplanned equipment downtime because maintenance teams must manually search through disparate schematics, PLC code, sensor data, and maintenance logs to diagnose faults. This fragmented information leads to slow fault identification, higher labor costs, and loss of institutional know‑how.
Solution
Edmund provides an industrial AI platform that ingests and links electrical schematics, PLC programs, IoT telemetry, and historical maintenance records into a unified, searchable knowledge graph. An always‑on AI assistant interprets error codes, traces root causes across data sources, and delivers step‑by‑step repair instructions to technicians. The system continuously learns from each fix, updating its recommendations and generating automated maintenance reports. By presenting contextual insights in plain language, Edmund reduces the time needed to locate and resolve faults, helping factories cut downtime and retain expertise within the organization.
Target Audience
Primary users are maintenance engineers, technicians, and production managers in mid‑size to large manufacturing facilities that operate complex automated lines and need faster, data‑driven fault resolution.
Features
- Automatic extraction of components and wiring logic from electrical schematics to build a machine connectivity graph
- Linking of PLC function blocks to physical components for real‑time process understanding
- Integration of live IIoT streams (e.g., sensor telemetry, alarm data) with the connectivity graph for instant fault mapping
- Unified search across manuals, datasheets, 3D drawings, and maintenance journals with context‑aware results
- AI‑driven error interpretation that translates PLC and sensor codes into plain‑language explanations
- Guided repair workflow that provides step‑by‑step instructions and logs each action for future learning
- Automated maintenance automation (preventive scheduling, condition‑based triggers, smart reporting) to reduce manual tasks
- Secure, multi‑factory deployment on an industrial‑grade data stack (Snowflake, PostgreSQL, Oracle, InfluxDB)