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Tilores Identity RAG
Overview :
Tilores Identity RAG is a platform providing customer data search, unification, and retrieval services for large language models (LLMs). It uses real-time fuzzy search technology to handle spelling errors and inaccurate information, delivering accurate, relevant, and unified customer data responses. The platform addresses challenges faced by large language models when retrieving structured customer data, such as data being spread across various sources, difficulties in finding customer data due to incomplete matching of search terms, and the complexities involved in unifying customer records. It allows for quick retrieval of structured customer data, the construction of dynamic customer profiles, and provides real-time, unified, and accurate customer data during queries.
Target Users :
The target audience includes data scientists, corporate IT decision-makers, and companies dealing with large volumes of customer data. Tilores Identity RAG is suitable for them as it provides a fast, accurate, and scalable solution for unifying and retrieving customer data, thereby enhancing the quality and efficiency of customer service.
Use Cases
Voter fraud detection in the U.S.: Use Tilores Identity RAG to unify and retrieve voter data to detect fraudulent activities.
Company registration information queries in the U.K.: Quickly retrieve company registration information through the API provided by Tilores Identity RAG.
CRM data deduplication: Utilize Tilores Identity RAG to unify customer data in CRM systems and reduce duplicate records.
Features
Real-time fuzzy search: Handles spelling errors and inaccuracies to provide accurate customer data responses.
Data unification: Utilizes fuzzy matching techniques to unify data from different source systems, even when attributes differ.
Rapid retrieval: Quickly retrieves structured customer data to build dynamic customer profiles.
Scalability: Facilitates swift deployment through LangChain integration and data connectors, utilizing managed and distributed infrastructure to scale customer data.
API support: Offers APIs for real-time unification of dispersed customer data.
Privacy protection: Ensures the privacy and security of customer data.
Easy integration: Seamlessly integrates with existing LLMs and data source systems.
How to Use
Step 1: Create a free Tilores account.
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Step 2: Experiment with LangChain integration on GitHub.
Step 3: Develop an LLM application based on Tilores Identity RAG.
Step 4: Connect Tilores to your LLM to search customer data dispersed across multiple source systems.
Step 5: Utilize real-time fuzzy search techniques to handle spelling errors and inaccurate information in search terms.
Step 6: Employ data unification features to consolidate data from different source systems into a single customer record.
Step 7: Retrieve and unify customer data in real-time through API calls.
Step 8: Build dynamic customer profiles using unified customer data and provide accurate data upon query.
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