Traditional RAG works great with text.
But telecom networks speak many languages.
- Logs.
- Packet traces.
- KPI dashboards.
- Network diagrams.
- Alarm streams.
When a VoNR call fails or a PDU session drops, engineers don’t look at just one document. They correlate signals across multiple systems and data types.
This is where Multimodal RAG becomes powerful.
Instead of retrieving only text, it brings together:
- Network logs from core nodes
- Wireshark packet traces
- KPI trends and performance data
- Alarm events from NMS
- Architecture diagrams and call flows
By embedding and retrieving these different signals together, AI can connect the dots across the network and assist engineers in identifying the root cause faster.
In complex telecom environments, this can turn AI into a true troubleshooting co-pilot.
I explored this idea in more detail here 
https://medium.com/@jayanthi.syamala/multimodal-rag-in-telecom-industry-4d56027f87cb
(visual below shows how Multimodal RAG correlates telecom signals)
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