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

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: https://tilores.io/RAG Go straight to the GitHub repo for our LangChain integration: https://github.com/tilotech/lang... Or read this Medium article for more context about Identity RAG: https://bit.ly/3TSwe22

@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.

@cschagen that is certainly something we can help you with!

@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

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.

Well done on launching Tilores! A great step forward in streamlining data search and retrieval.

Thank you @ditarth_wbs. We also want to ensure that your customers get the best service and the data stays private.

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Tilores

Customer data search, unification and retrieval for LLMs

Customer data search, unification and retrieval for LLMs

Tilores

Customer data search, unification and retrieval for LLMs

Customer data search, unification and retrieval for LLMs

Do you use Tilores?

Do you use Tilores?