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Use Case

Give your LLM a unified
view of every customer

IdentityRAG adds identity resolution to your RAG pipeline. Your AI gets complete, deduplicated customer context — not fragments from a single database.

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The Problem

Standard RAG fails on customer data

Without IdentityRAG

User asks: "What's Sarah Johnson's order history?"

✗Finds "Sarah Johnson" in CRM (3 orders)
✗Misses "S. Johnson" in Shopify (8 orders)
✗Misses "SARA JOHNSON" in ERP (3 orders)
✗LLM reports 3 orders (actual: 14)
With IdentityRAG

Same question, different result:

✓Tilores resolves all 4 records to one entity
✓Golden record: 14 orders, 2 emails, 4 systems
✓LLM gets complete context in <150ms
✓Accurate, complete answer every time
How It Works

Four steps to customer-centric AI

1
User Query

User asks about a customer by name, email, or any attribute.

2
Identity Resolution

Tilores finds all records for this person across all systems in <150ms.

3
Golden Record

A unified, deduplicated customer profile is assembled with full history.

4
LLM Response

The LLM generates an accurate answer based on the complete customer view.

Integrations

Works with your AI stack

LangChain

Drop-in LangChain retriever. pip install langchain-tilores

Amazon Bedrock

Use Claude, Titan, or any Bedrock model with identity-resolved context.

Python SDK

Full Python SDK for custom integrations. pip install tilores-sdk

View the open-source reference implementation on GitHub →
Explore

Other use cases

⚠
Fraud Detection

Detect fraud rings and linked accounts in real-time
👥
Customer Identity Intelligence

Build a truly complete view of every customer
✓
KYC & Compliance

Accelerate identity verification and reduce false positives

Build customer-centric AI
in minutes, not months

See IdentityRAG in action with your own data. Available on AWS Marketplace.

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