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What is Tilores?
Tilores IdentityRAG is an advanced AI tool designed to unify and provide real-time access to scattered customer data from multiple internal sources through a natural language interface. Built by Tilores, it leverages IdentityRAG technology combined with LangChain and Amazon Bedrock to enhance Large Language Model (LLM) chatbots with accurate, dynamic customer profiles. The tool solves the problem of fragmented customer data by performing advanced identity resolution using multiple attributes such as name, address, email, and phone number. It enables customer success, sales, marketing, and support teams to query unified customer data instantly, improving decision-making and customer interactions. Tilores fits workflows that require real-time, scalable, and accurate customer 360° views, especially in industries like finance, retail, and SaaS where data is dispersed across CRM, support, marketing, and loan systems.
AI Tool Review Summary
4.7/5
Accurate, comprehensive, and context-rich
Intuitive and streamlined natural language chat interface
To unify scattered customer data and provide real-time, accurate customer insights to LLM chatbots.
Integrates seamlessly with CRM, support, marketing, and loan databases via API and LangChain framework.
Offers early access with custom pricing; likely enterprise-focused with scalable plans.
Features
Features with the highest value for users are highlighted here.
Identity resolution algorithms
Data unification across sources
Real-time customer data updates
Integration with LangChain and Bedrock
Natural language interface for queries
Dynamic customer profile building
Serverless scalable infrastructure
Who Is It For?
Customer Success Teams
Sales Teams
Marketing Teams
Data Engineers
Product Managers
Fraud & Risk Analysts
SaaS Companies
Small to Medium Businesses
Enterprise IT Teams
Developers
Pricing
Early Access
Access to live demos Integration with multiple data sources Real-time customer data unification Advanced identity resolution
Join the Command Staff.
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Pros & Cons
Pros
Provides accurate and unified customer profiles in real-time Enables natural language interaction with complex customer data
Cons
Requires integration with multiple data sources for full functionality May have a learning curve for teams new to LLM frameworks
FAQs
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