What happens when an AI assistant needs answers from more than one source? One of the challenges ...What happens when an AI assistant needs answers from more than one source? One of the challenges ...
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Abdul Wahhab
• Aug 19
What happens when an AI assistant needs answers from more than one source?
One of the challenges with building useful AI assistants isn't just generating a response.
It's making sure the response is based on the right information at the right time.
For FLT, the assistant needed to work across multiple data domains while supporting both text and voice interactions.
Instead of relying on a single static knowledge source, we built the system around a Retrieval-Augmented Generation (RAG) pipeline.
The approach connected authenticated APIs, SQL and JSON data sources so the assistant could retrieve relevant information before generating its response.
We also integrated:
→ Vapi for voice and chat interactions → Twilio for calls and SMS → RAG for contextual retrieval → SQL & JSON sources for dynamic data → Secure authentication for personalized experiences
The result was a full-stack AI assistant capable of delivering contextual responses across both chat and voice.
The key takeaway:
An AI assistant becomes much more useful when it can retrieve the right context instead of relying only on what the model already knows.
ai
rag
That's where the architecture behind the AI becomes just as important as the AI itself.
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