Tilores provides an API that unifies scattered customer data in real-time, enabling data-driven companies to create a comprehensive Customer 360 view across various source systems. This solution addresses the challenges of inconsistent and siloed data, allowing businesses to manage risk, detect fraud, and enhance customer experiences without requiring extensive engineering resources.
Funding
$3.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Businesses struggle with fragmented and inconsistent customer data spread across multiple source systems, hindering their ability to gain a comprehensive understanding of their customers. This siloed data makes it difficult to align internal teams, manage risk effectively, detect fraud, and deliver personalized customer experiences.
Solution
Tilores offers an API that unifies scattered customer data in real-time, providing a comprehensive Customer 360 view. The platform cleanses, normalizes, and enriches customer data from various sources, syncing unified profiles back to each department's system. By connecting to source systems through a globally distributed GraphQL API or prebuilt connectors, Tilores automates data unification, eliminating engineering complexity. This enables businesses to build real-time solutions for risk management, fraud detection, and personalized experiences, as well as power Retrieval-Augmented Generation (RAG) applications with accurate customer data.
Target Audience
Tilores primarily targets data-driven companies seeking to create a unified customer view, including those focused on risk management, fraud detection, customer experience enhancement, and GenAI application development.
Features
- Real-time customer data unification across disparate source systems
- Globally distributed GraphQL API and prebuilt connectors for Salesforce, Hubspot, and Snowflake
- Automated data cleansing, normalization, and enrichment to match a unified schema
- Fuzzy matching tools for enhanced identity resolution
- Transparent reasoning for each profile merge and relationship mapping
- Scalable infrastructure for handling both new and historical customer data
- Retrieval-Augmented Generation (RAG) capabilities for LLMs