Tilores Identity RAG Features
Tilores Identity RAG is a platform providing customer data search, unification, and retrieval services for large language models (LLMs), using real-time fuzzy search technology to deliver accurate, relevant, and unified customer data responses.
View MoreKey Features of Tilores Identity RAG
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 system connects to multiple source systems, unifies scattered customer data, and enables LLMs to use this unified data for answering queries or as context for subsequent unstructured data queries.
Fuzzy Search: Utilizes real-time fuzzy search technology to handle misspellings and inaccuracies, ensuring accurate and relevant customer data retrieval.
Data Unification: Unifies scattered customer data from different source systems using fuzzy matching, even when attributes aren't identical.
LLM Integration: Seamlessly integrates with LLMs through LangChain, allowing them to access and utilize unified customer data for query responses.
Scalable Infrastructure: Offers managed and distributed infrastructure to scale customer data processing alongside LLM growth.
Real-time Dynamic Profiling: Builds dynamic customer profiles at query time, providing LLMs with access to up-to-date unified customer data.
Use Cases of Tilores Identity RAG
Voter fraud detection: Use Tilores Identity RAG to unify and retrieve voter data to detect fraudulent activities in the U.S.
Company registration information queries: Quickly retrieve company registration information through the API provided by Tilores Identity RAG in the U.K.
CRM data deduplication: Utilize Tilores Identity RAG to unify customer data in CRM systems and reduce duplicate records.
KYC and AML compliance: Leverage Tilores Identity RAG for real-time customer data monitoring and compliance checks in financial services.
Pros
Improves LLM accuracy with unified and real-time customer data
Handles misspellings and inaccuracies through fuzzy search
Scales easily with managed infrastructure
Offers quick integration through LangChain and data connectors
Cons
May require initial setup and configuration for data sources
Potential privacy concerns when unifying customer data from multiple sources
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