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.
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
About Tilores Identity RAG on Product Hunt
“Customer data search, unification and retrieval for LLMs”
Tilores Identity RAG launched on Product Hunt on October 2nd, 2024 and earned 321 upvotes and 75 comments, earning #3 Product of the Day. 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.
On the analytics side, Tilores Identity RAG competes within Artificial Intelligence, Data and Data Science — topics that collectively have 472.2k followers on Product Hunt. The dashboard above tracks how Tilores Identity RAG performed against the three products that launched closest to it on the same day.
Who hunted Tilores Identity RAG?
Tilores Identity RAG was hunted by Steven Renwick. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Tilores Identity RAG including community comment highlights and product details, visit the product overview.
Steven Renwick