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Tilores Identity RAG - Customer data search, unification and retrieval for LLMs

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Data scientists connect Tilores to their LLM to search internal customer data scattered across multiple source systems. The LLM retrieves unified customer data, which it uses to answer queries or as context when querying subsequent unstructured data.

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This is interesting, will share with my engineering colleagues right away. We had good experiences using Tilores' other product in our risk engine, but the RAG product could really solve some issues in our LLMs. Quick question: can you shed some light on scalability?
Maker
so Tilores is designed to be highly-scaling with zero input required, since we use serverless technology. So we can ingest as much data and provide as many searches, in parallel, as you could ever need. The only thing you would have to keep an eye on is the cost of the LLMs themselves, since that is outwith our control.
Congrats to the team on this milestone! Excited to see how Tilores is evolving!
Maker
your support and the support of the good data people, has been invaluable!
wow , hats off. love the idea of seamlessly unifying scattered data - sounds like a game-changer.
Maker
it really is and it is soo easy to use
god speed tilo team! forever in your corner. 🔥🔥
Maker
Thank you for your support Madeline!
Maker
Thank you :-)
Great work, love this!
Maker
thank you
Tilores really simplifies the process. I especially love the fuzzy search that corrects inaccuracies, such a smart feature. Looking forward to seeing this in action!
Maker
thanks Sage. People often forget about how important fuzzy search is, but it deals with the reality of real-world data - it is messy and often contains inaccuracies.
Maker
thanks looking forward to see you testing
Following this launch closely
Maker
good man!
Wow, , this is truly a game-changer for data scientists and enterprises leveraging LLMs! 🚀 The fragmentation of customer data has always been a significant hurdle, and Tilores seems to offer a seamless and efficient solution to this problem. The fact that it was initially developed for high-stakes environments like fraud prevention and AML really speaks to its robustness and reliability. Integrating Tilores with LangChain to unify and streamline customer data is brilliant. The potential for enhanced customer interactions and more accurate query responses is immense. Plus, the emphasis on GDPR compliance and data security is crucial for businesses operating within European standards. The $500 free credit offer is a generous touch and a great incentive for the Product Hunt community to dive in and explore the capabilities of Tilores. Kudos to the team for creating something that can significantly elevate AI-driven customer experiences. Can't wait to see how this transforms the landscape for LLM applications! 🌟
Maker
Maker
glad you like it. Thanks for your comment!
Impressive integration with LangChain for unified customer data. This could significantly enhance AI-driven customer experiences. How do you handle data freshness and consistency when aggregating information from multiple sources in real-time?
Maker
data can be ingested from different sources in real-time while resulting in consistent data. If you are interested in the details let’s talk
Maker
hi Olia. Good question. The real-time data ingestion is a major feature of Tilores. Other systems might do this in batch, but Tilores will literally ingest customer data from any source via API, in real-time regardless of volume. It doesn't matter if it is 1 record per second or 1,000.
Congrats to the Tilores team on the launch of Identity RAG! This sounds like a powerful tool for streamlining access to unified customer data. Are there any specific integrations available for different source systems to enhance data retrieval?
Maker
there is a snowflake and webhook integration, howeverwe also provide a graphql API and a python sdk which easily integrates with most systems
Maker
our most used integrator is actually for Snowflake. Other than that, using our GraphQL API you can connect to any data source or we can discuss building a specific connector for you for specific sources.
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