Datch Diagnostic Agent

What It Does

Datch Diagnostic Agent is an autonomous AI system built specifically for industrial manufacturing and plant operations. It analyzes equipment failures, predicts root causes, and guides frontline technicians through troubleshooting steps—cutting diagnostic time by up to 15x. By connecting scattered operational data from CMMS/EAM systems, OEM manuals, work orders, schematics, and alarm logs, the agent delivers instant, contextualized repair guidance to operators and maintenance teams on the plant floor.

Who It's For & The Problem It Solves

Designed for manufacturing plants, industrial facilities, and asset-intensive operations, Datch addresses the chronic challenge of unplanned downtime and slow mean-time-to-repair (MTTR). When equipment fails, technicians waste hours hunting through manuals, calling senior engineers, or guessing at solutions. Datch eliminates that guesswork by putting the collective knowledge of the entire enterprise—including retiring subject matter experts—into an always-on AI agent that learns and improves over time.

Key Features & Capabilities

• Customized Ontology: Aligns fragmented data from multiple systems to your specific operational goals and asset taxonomy
• Precision Data Extraction: Automatically pulls critical insights from PDFs, PowerPoints, Word docs, and legacy CMMS records
• Cortex™ Intelligence Engine: Proprietary graph-enabled services and agentic workflows orchestrate multi-step diagnostic reasoning
• Adaptive Learning: Incorporates real-time user feedback and past fixes to continuously improve accuracy and relevance
• Knowledge Gap Detection: Identifies missing documentation so teams can prioritize updating critical knowledge bases
• Institutional Knowledge Retention: Captures expertise from experienced technicians before they retire, making it accessible to new hires

Typical Workflows & Use Cases

When a machine goes down, a technician opens the Diagnostic Agent on a mobile device or workstation. The agent analyzes the failure symptoms, cross-references historical work orders and alarm data, then recommends specific troubleshooting steps and likely root causes. As the technician provides feedback (e.g., "that fixed it" or "still not working"), the agent refines its suggestions in real time. Over weeks and months, the system learns plant-specific failure patterns, becoming more accurate and reducing repeat issues.

What Differentiates It

Unlike generic ChatGPT interfaces, Datch was purpose-built for chaotic industrial data environments—paper-based records, incomplete metadata, mislabeled files. It thrives in the messiness typical of manufacturing plants. The company reports jaw-dropping adoption rates even among skeptical veteran technicians, with site managers noting boosts in team morale. Datch is backed by enterprise leaders and has demonstrated measurable impact: clients report shrinking plant downtime by 10% and significant improvements in Overall Equipment Effectiveness (OEE).