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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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Maker
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Hi Makers! I'm Steven, one of the founders of Tilores. I've very excited to introduce our LangChain integration to you so you can use Tilores as a data source for "Identity RAG". As companies increasingly turn to Large Language Models (LLMs) to enhance customer interactions, a common challenge arises: customer data is often fragmented across multiple internal databases and systems. This fragmentation makes it hard for LLMs to provide reliable, accurate responses based on complete, up-to-date information. Tilores solves this by offering a real-time API that unifies scattered customer data. Originally developed for a European consumer credit bureau to power fraud prevention and anti-money laundering solutions, Tilores' "identity resolution" technology is now available to supercharge LLMs through an integration with LangChain, the leading LLM framework. With Tilores, you can: 🖇️ Seamlessly connect all your customer data sources, including valuable metadata like orders, transactions, and more. ⚖️ Build a unified "source of truth" for your LLM, ensuring it always has access to complete and relevant customer insights. ⚡ Perform lightning-fast searches and updates, keeping your LLM working with real-time data. 🤖 Use your preferred LLM within your own infrastructure—your data stays securely within your systems. 💾 Enjoy automated scaling, enterprise-grade reliability, and GDPR compliance, all tailored to European data privacy standards. 🙌🏻 Empower your LLMs with unified customer data, and take your AI-driven customer experiences to the next level with Tilores. Tilores is designed to be used for structured customer data alongside a vector database for unstructured data to give you the ultimate enterprise LLM experience. For anyone from Product Hunt building a LLM based on Tilores' Identity RAG, we will offer you $500 of free credit to get started. You can also visit our website: Go straight to the GitHub repo for our LangChain integration: Or read this Medium article for more context about Identity RAG:
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.
Maker
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.
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.
Maker
that is certainly something we can help you with!
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
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!
Maker
Hi 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
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.
Well done on launching Tilores! A great step forward in streamlining data search and retrieval.
Maker
Thank you . We also want to ensure that your customers get the best service and the data stays private.
Exciting Steven and team!
Maker
Thank you
Congrats on the launch! 🚀 Connecting Tilores to LLMs for unified customer data retrieval is a powerful tool for data scientists. Looking forward to seeing how it streamlines customer data management and enhances query capabilities across multiple systems!
十分硬核的技术。
Maker
谢谢。我们有非常过硬的工程师。
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?
Maker
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 of Tilores Identity RAG! 🚀 I’ve created an interactive demo of your product using Supademo to showcase its features in action. You can check it out here: Supademo allows you to create engaging, interactive demos in minutes, and it's a great way to highlight key aspects of your product for potential users. I'd love to see this demo added to your launch page—it could help drive more engagement during your launch and beyond! Feel free to share it with your community and let us know if you’d like any updates or adjustments. Best of luck with your launch!
Maker
Thank you that is a nice tool that you created there.
Maker
that's pretty cool actually.
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.
Maker
Thank you . 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.
Excited to check it out thanks Tilores team
Maker
let us know if you have any questions.
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