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Product

Entity resolution
infrastructure for developers

Tilores ingests customer data from any source, resolves duplicates in real time, and serves unified entity profiles via GraphQL. Run it fully managed on AWS with no infrastructure to operate, or deploy the same engine inside your own environment. It does three things, brilliantly.

Book a Demo Try Tilores Studio (Free)

AWS-native by default · Infrastructure-agnostic by design · Runs in your cloud, your data centre, or your laptop

How Tilores works

Three steps from raw records to answers

Each step is a capability in its own right — click into any of them for the technical detail and a live, interactive walkthrough.

Step 1

Extract & Transform

Ingest from anywhere, then clean it up.

Push records from any source in any shape. Tilores maps, normalizes, and enriches every field — names cased, phones to E.164, addresses parsed — so matching compares like with like.

Any schema, no migrationNormalize & standardizeEnrich before matching
Step 2

Match & Resolve

Turn scattered records into one entity.

Deterministic rules and fuzzy matching draw explainable edges between records and assemble them into a single resolved entity — with a transparent audit trail and a quality score.

Rule-based + probabilisticFuzzy & phonetic matchingExplainable edges & score
Step 3

Search & Insights

Query the resolved graph in milliseconds.

Fetch an entity by any record, search fuzzily ranked by hitScore, aggregate across every source with recordInsights, and run what-if analysis — all over one real-time GraphQL API.

GraphQL, sub-150ms on AWSRanked fuzzy searchInsights & what-if
Architecture

Three components. Any infrastructure.

Tilores depends on three generic things: a key-value store, a queue, and file storage. On AWS that's DynamoDB, SQS, and S3, running serverless with nothing for your team to operate. Anywhere else, it's whatever products already fill those roles. AWS-native by default, infrastructure-agnostic by design.

<150ms
query latency on AWS (1ms self-hosted)
3
infrastructure components required
0
servers to operate on managed AWS
New: IdentityRAG

Give your LLM a unified
view of every customer

IdentityRAG connects Tilores entity resolution to LangChain and Amazon Bedrock. Your AI chatbot gets a complete, deduplicated customer 360 — not fragments from a single database.

Retrieves unified customer profiles before generating LLM responses

Creates accurate golden records from disparate data sources

Response times under 150ms for identity resolution on AWS, 1ms self-hosted

Works with any LLM via LangChain integration

View on GitHub Read the Guide
IdentityRAG Flow
1
User asks
"What's Sarah Johnson's order history?"
2
IdentityRAG resolves
Finds all records for Sarah across CRM, Shopify, ERP, Support
3
Golden record created
Unified profile with 14 orders, 2 emails, 4 source systems
4
LLM responds
"Sarah has 14 orders totaling €2,847. Her last order was..."
Why Tilores

Tilores vs. the alternatives

Building entity resolution in-house takes years. Existing tools aren't built for real-time. Tilores gives you both.

Feature
Tilores
AWS ER
DIY
Real-time resolution
✓
✗
✗
Sub-150ms latency (1ms self-hosted)
✓
✗
✗
GraphQL API
✓
✗
✗
No infrastructure to manage (managed option)
✓
✗
✗
Deployable in your own environment
✓
✗
✓
No ML expertise required
✓
✓
✗
Fuzzy matching built-in
✓
✓
✗
Prebuilt connectors
✓
✓
✗
GDPR compliant
✓
✓
✓
Time to production
Days
Months
Years

Ready to unify your customer data?

Talk to our team about your use case, or download the evaluation build to try it with your own data.

Book a Demo Try Tilores Studio (Free)