Complexity and Volume of Manuals
Maintenance manuals are extensive and complex, requiring technicians to constantly reference them, leading to a high Mean Time to Repair (MTTR). According to McKinsey & Company, companies spend around 70-85% of technician hours on preventative maintenance, improving overall equipment effectiveness (OEE) and reducing costs.
Human Error and Safety
Manual referencing leads to errors, affecting productivity and safety. High-risk environments require reliable information to avoid accidents. According to Infraspeak Blog, unplanned equipment downtime can cost industrial manufacturers up to $260,000 per hour.
Data Management and Accessibility
Companies struggle with managing and retrieving information from vast amounts of unstructured data in manuals and operating procedures. McKinsey & Company states that maintenance typically accounts for between 10% and 25% of total operating costs.
AI-Powered Search
MENTOR leverages the latest Large Language Models (LLMs) to find relevant maintenance information quickly. It uses NextGen Retrieval Augmented Generation (RAG) to customize search results specific to industry manuals without needing full model retraining and seamlessly processes text, structured text (tables, image callouts, etc.), audio, and image data. Users see an 85% reduction in MRO data retrieval time with faster access to maintenance repair information.
Local, Scalable Deployment
MENTOR operates on laptops and/or tablets at the point of repair without requiring an internet connection. Our local integration capabilities ensure data security and accessibility in low-connectivity environments. MENTOR is tailored to specific machinery needs with scalable deployment options, increasing labor productivity by 10-25% by augmenting your workforce with AI-driven tools.
Simple Interface
MENTOR’s chat-like interface enables natural language for quick and accurate information retrieval, continuously improving results as users interact with the platform. It offers a Visual Language Model (VLM) integration for advanced image and text analysis. Users see a 15% increase in equipment efficiency with a focus on delivering complex AI packaged into an intuitive interface that anyone can use.
Effeciency & Productivity
Improve the speed of your operations with MENTOR. Tap into precise maintenance procedures and troubleshooting steps instantly. We reduce MTTR by providing quick access to necessary information, enhancing productivity while minimizing downtime. Reduce mechanical downtime by 20-40% by finding the right answer to the right MRO question.
Safety & Accuracy
Enhance the safety of your operations and reduce human error with MENTOR. Our software provides technicians with accurate and reliable information to navigate hazardous environments. Boost operational compliance rates and worker safety by 40%, less time spent trying to fix something using the wrong information.
Cost Savings
MENTOR lowers operational costs by reducing downtime and improving maintenance efficiency. Long after a company’s initial investment in the MENTOR technology, firms reap the benefits of long-term savings. By automating MRO processes, MENTOR can reduce Operational Overhead Costs by 10-20% and promote stickier client relationships with NextGen Digital Experiences.
1. Document Parsing and Ingestion
MENTOR uses advanced OCR and deep learning techniques to extract and index all of the data from unstructured documents.
2. Vector Database Storage
Stores all content from parsed documents in a vector database for fast and accurate similarity search.
3. LLM Retriever-Generator
Retrieves relevant context from the knowledge base and generates accurate answers using LLMs.
4. User Interface
Provides an intuitive chat-like interface for easy interaction and information retrieval.
Industrial Products Manufacturing
Enhancing MRO Data Retrieval and Operational Efficiency
Streamlining Document Search for Pipeline Projects
Prototype
- 3 user licenses
- X # of documents
- Able to handle most images, tables, image callouts, handwritten notes
- Relevancy scoring, light contextualization and VLM testing
- Deployed in 3 months
Premium
- 10 user licenses
- x # of documents
- Processing on all data modalities
- Context engine tuned to specific documents and requirements, VLM prototype integration
- Deployed in 5 months
Enterprise
- 25+ user licenses
- X of documents
- Processing all data modalities, including VLM integration
- Deployment timelines ~9-12 months