Conversational AI that turns factory data into fast answers
A simple, manufacturer-focused guide to conversational AI, RAG, and practical rollout steps that reduce reporting friction.
What You'll Learn
- What conversational AI is and how it differs from predictive AI and computer vision
- How it connects to MES, ERP, SCADA, QMS, and PLM for grounded answers
- The simple “Retrieve, Augment, Generate” (RAG) pattern for data-tied responses
- Real questions teams ask about OEE, downtime, cycle time, and deviations
- What conversational AI does well today: search, summarize, compare, explain
- A rollout path: start small, prove value, expand sources as trust grows
Why It Matters
- Faster time-to-answer across systems with one natural-language question
- Less report dependency and fewer “can you pull this?” requests
- Quicker issue resolution (reported up to 3× faster)
- Reduced report creation workload (reported up to 65% less)
- Better alignment in daily meetings using current, consistent numbers
What You Can Share with Others
- A plain-language definition of conversational AI for manufacturing teams
- A one-page RAG explainer (Retrieve → Augment → Generate)
- A starter list of shop-floor and quality questions to pilot first
- A rollout checklist: Connect → Ask → Expand (high-value questions first)
Download the eBook (PDF)
Get the 13-page guide and start with a few high-value questions—no form, just practical clarity.
FAQ
Q: What is conversational AI in a factory context?
A: Software you can talk to in plain language that answers using facts from your connected systems.
Q: Is conversational AI the same as predictive AI or computer vision?
A: No. Conversational AI explains and summarizes what already happened; it doesn’t auto-tune equipment or inspect images.
Q: What data sources can it use?
A: Common sources include MES, ERP, SCADA, QMS, and PLM—often starting with MES and quality first.
Q: What is RAG and why does it matter?
A: RAG (Retrieval-Augmented Generation) keeps answers tied to your data by retrieving relevant records, adding them as context, then generating a response.
Q: What are good first use cases?
A: Downtime drivers, today’s OEE, open deviations by batch, stations over cycle time during changeovers, and top unplanned downtime reasons.
Q: Will it control machines or run real-time control loops?
A: No—think of it as “search and explain” for operational data to support human decisions.