Run your conversations through the model you chose in AWS Bedrock and act on what it finds.
Get more done with Tidio and Pinecone integration
Use Pinecone to find the answer that resolved a Tidio conversation like this one before.
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Integration availability depends on your Tidio plan.
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Category
AI & LLMs
Integration availability depends on your Tidio plan.
Compare plans
Use cases
What you can do with Tidio and Pinecone
Find the right article
A Pinecone query turns a customer's own wording into the help article that answers it.
Reuse a past resolution
The conversation that solved this last month is retrieved instead of rediscovered.
Ground an AI answer
Retrieved passages give a model something real to answer from.
Keep the index current
New articles and resolved chats are upserted into Pinecone as they land.
Overview
Pinecone stores embeddings and finds the nearest match, which is exactly what you want when a customer describes a problem in their own words. Indexing your help articles and resolved conversations means a Tidio chat can surface the article that fits, or the ticket somebody solved last month, without depending on the customer using your vocabulary. Pinecone stays the index. The conversation asks it a question and gets candidates back.
Key features
- Search by meaning A Pinecone query matches the intent, not the keywords.
- Past resolutions reused Solved conversations become searchable answers.
- Namespaces per source Keep articles, tickets and docs separate in Pinecone.
- Fresh index, useful answers Upserts keep the index current as content changes.
Pinecone
AI & LLMs
Tags
Custom Integration
Workflow Automation
How it works
Connect Pinecone in minutes
1
Connect Pinecone and Tidio
A service embeds the message, queries Pinecone and posts candidates to the Tidio API.
2
Index the right sources
Help articles, resolved conversations and internal docs, each in its own namespace.
3
Keep the loop closed
Resolved conversations are embedded and upserted, so the index improves as you work.
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Frequently asked questions
Common questions about the Pinecone integration
Do we need a model as well as Pinecone?
Yes, for the embeddings on both sides: the documents you index and the message you query with. Pinecone stores and searches the vectors, it does not create them. Pick one embedding model and stay on it, because mixing models in one index produces confident nonsense.
Should retrieved answers go straight to customers?
Show them to the agent first. Retrieval is good at finding the right area and occasionally wrong in a way that reads plausible. An agent glancing at three candidates and picking one is fast, and it keeps a person between Pinecone and the customer.
Is the Pinecone integration available on my plan?
Availability depends on the Tidio plan you are on. Compare plans to see which plan covers the Pinecone integration.