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Tilores Identity RAG — Customer data search, unification and retrieval for LLMs
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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@major_grooves Interesting. So in my app, we crawl travel data from across hundreds of travel sites, articles and feed those data packets onto LLM based on user's query. Is there anyway Tilores can assist in the entire process? Super congratulations on the launch.
@akshay_lahri how do you currently feed that data into the LLM? A vector database? If you end up with lots of duplicate records when you are crawling, Tilores might be able to help you deduplicate them, but tbh when it comes to the unstuctured text in a typical website article, you might be best sticking with a vector database.
@major_grooves congrats, this is really useful. We have a lot of duplicative records in our CRM, especially when folks move on from one co to another, this could be a great extra layer to remove one frequent failure mode.
@major_grooves congratulations on the launch. Cool product! How does Tilores RAG deal with data versioning? So how does it track the change in data over time and can I perform a search based on a specific point in time? Cheers
@irhymeth your support and the support of the good data people, has been invaluable!
Nice, will recommend it to my marketing agency. I love the RAG feature.
Does it also support real time syncing my contacts across LinkedIn, Google Sheets, Eventbrite, Meetup and HubSpot in one database?
@arthurpoot yes - as long as we can access them via API we can both pull data from them and push data back to keep them updated - in real-time. Glad you like it!
Congratulations on the launch. It looks really cool and powerful, and it seems like something that will simplify many use cases. I'm looking forward to seeing what people build with it.
Thank you @janoberhauser. I opens the door for so many different use cases and allows all Python users to easily access their data in Tilores. I am looking forward to see people using it from within N8N.
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?
@vietpham there is a snowflake and webhook integration, howeverwe also provide a graphql API and a python sdk which easily integrates with most systems
@vietpham 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.
Congratulations Tilores! Data scientists can now seamlessly link LLMs to scattered internal customer data, ensuring smarter queries and better results. This is a game-changer! 💡 #AIInnovation #DataIntegration
Wow, @major_grooves, 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! 🌟
How nice is that! That seems like a an amazing piece of technology. Truly innovative! Will test it soon!!!
wow @major_grooves, hats off.
love the idea of seamlessly unifying scattered data - sounds like a game-changer.
I'm really intrigued by Tilo’s RAG product! The ability to enhance document retrieval and generate accurate answers from various data sources sounds incredibly powerful. I love how it combines retrieval-augmented generation with AI to provide contextually relevant responses. The potential for improving decision-making processes and user experiences is fascinating. I'm especially curious about how it integrates with existing systems and the ease of implementation. This could be a game changer for businesses looking to leverage their data more effectively. I can't wait to learn more about its capabilities!
It's so amazing! I'm thinking about how to integrate RAG into my website https://quitporn.ai. And today I found your tool. It's a perfect example for me to learn and use. Congrats to your team!
Hey Steven,
How quickly can the system update and retrieve unified customer profiles?
Have you considered expanding beyond customer data to other domains where entity resolution could be valuable?
Congrats on the launch!
Hi @kyrylosilin the process to update a profile takes less than 500 ms. - No matter how many profiles change at the same time.
We are also using Tilores in other spaces like company data.
Thank you
Hendrik
@kyrylosilin I found their API to be REALLY fast, even at scale. And what I found really nice comparing to other entity resolution systems, you can define the golden record at read-time, as opposed to at write time. This way you can get different perspectives on the same source data.
For example, for some applications you might want to have the latest email of an unified customer.
For some applications you might want to have all emails that belong to one unified customer.
The ability to define different golden records at read-time makes Tilores really flexible.
Tilores