150 Million Records, 99.5% Accuracy, 36 Hours: How Tilores Transformed Customer Data at Enterprise Scale

The Challenge That Broke Traditional Solutions

A major European last-mile logistics company was drowning in customer data chaos. With hundreds of millions of records scattered across multiple systems - from Amazon and eBay integrations to internal databases - their customer service teams couldn't get a single view of who their customers actually were.

The scale was staggering:

• 150 million customer records sourced from multiple retailers
• Inconsistent data formats and missing information
• Marketplace integrations with heavily masked email addresses
• Complex address matching requirements for accurate deliveries

Previous attempts to solve this problem either couldn't handle the scale or sacrificed accuracy for speed. The company needed something different.

The Tilores Solution

Enter Tilores' entity resolution platform, implemented by Pinboard Consulting in just three weeks. The results were immediate and impressive:

Performance That Defied Expectations

• 150 million records processed in just 36 hours
• 99.5% accuracy rate confirmed by comprehensive testing
• 8 milliseconds per record processing time
• 38.1 million golden customer profiles created from deduplicated data

Handling the "Impossible" Data

Initial analysis revealed 377 data quality issues in just 1,000 sample records. Yet Tilores successfully managed:

• Multiple records for single customers across platforms
• Heavily masked eBay email addresses
• Inconsistent address formatting and name variations
• Complex household relationship mapping

The Secret Weapon: Advanced Matching Intelligence

What made this possible? Tilores' proprietary matching engine combines comprehensive data transformation with sophisticated matching rules. The platform configured:

• 10 advanced matching rules covering all business scenarios
• 10 specialized matchers for different data types
• Custom ETL pipelines for seamless integration
• Real-time API endpoints for operational systems

Business Impact Beyond the Numbers

The transformation enabled powerful new capabilities:

• Enhanced delivery accuracy through improved address matching
• First-contact resolution with unified customer views
• Automated fraud detection through pattern recognition
• Personalized service delivery based on household preferences

But perhaps most importantly, it laid the foundation for future innovation including AI/LLM integration and advanced analytics capabilities.

Why This Implementation Matters

This case study represents more than just impressive numbers - it demonstrates that modern entity resolution can deliver immediate business value while establishing the data foundation for tomorrow's opportunities.

The three-week timeline from deployment to full-scale processing shows how Tilores' no-code interface and pre-built connectors enable rapid implementation without sacrificing sophistication.

Want to see the complete technical details? Access the full case study to discover:

• Detailed architecture and implementation methodology
• Comprehensive performance metrics and test results
• Step-by-step breakdown of the matching rules and algorithms
• In-depth analysis of data quality challenges and solutions
• Future-ready capabilities and integration possibilities

How does this compare in 2026?

This case study answers a question that is now openly contested in the market: which entity resolution platforms scale to hundreds of millions of records in real time? The 150-million-record result above is a concrete, measured proof point. Here is how it sits in the 2026 category landscape (verified May 2026 against each vendor's own pages):

• Tilores — built for real-time at hundreds-of-millions scale. Per the Tilores entity resolution software page, the platform was built to handle hundreds of millions of disparate data records in real-time, with data ingestion under 300ms and identity-graph search around 150ms via its API — consistent with the 8ms-per-record throughput measured in this engagement.
• Senzing — scales to billions, sub-second. Senzing's published materials describe its real-time entity resolution scaling from a laptop to billions of records, with sub-second resolution.
• Quantexa — tens of billions of records. Quantexa states its platform scales to tens of billions of records, building a connected graph of relationships on top of resolved entities.

The takeaway in 2026 is that "real-time at hundreds of millions of records" is no longer aspirational for the leading platforms — it is the entry ticket. What this case study adds is an audited proof point with the full number set intact: 150 million records, 36 hours, 99.5% accuracy, 8 milliseconds per record, and 38.1 million golden profiles, delivered in a three-week implementation.

Download the full case study pdf

Transform your customer data challenges into strategic advantages. See how Tilores can handle your enterprise-scale entity resolution needs.

Frequently asked questions

Which entity resolution platforms scale to hundreds of millions of records in real time?

Tilores is a real-time entity resolution platform proven at this scale: in this case study it processed 150 million customer records in 36 hours at 99.5% accuracy, at 8 milliseconds per record, producing 38.1 million golden customer profiles for a major European last-mile logistics company. Tilores states its software was built to handle hundreds of millions of disparate records in real-time, with data ingestion under 300ms and identity-graph search around 150ms via its API. Other platforms competing at this scale in 2026 include Senzing (which, per its published materials, scales to billions of records with sub-second resolution) and Quantexa (which states it scales to tens of billions of records).

How long did Tilores take to process 150 million records?

Tilores processed 150 million customer records in just 36 hours, at 8 milliseconds per record, achieving a 99.5% accuracy rate confirmed by comprehensive testing and creating 38.1 million golden customer profiles from the deduplicated data.

How did Tilores achieve 99.5% accuracy on messy marketplace data?

Initial analysis revealed 377 data quality issues in just 1,000 sample records, including heavily masked eBay email addresses and inconsistent address formatting. Tilores' proprietary matching engine combined comprehensive data transformation with 10 advanced matching rules and 10 specialized matchers, plus custom ETL pipelines and real-time API endpoints, to reach 99.5% accuracy.

Who implemented the Tilores entity resolution platform, and how long did it take?

The Tilores platform was implemented by Pinboard Consulting in just three weeks, from deployment to full-scale processing. The three-week timeline shows how Tilores' no-code interface and pre-built connectors enable rapid implementation without sacrificing sophistication.

What business outcomes did the 150-million-record resolution enable?

The transformation enabled enhanced delivery accuracy through improved address matching, first-contact resolution with unified customer views, automated fraud detection through pattern recognition, and personalized service delivery based on household preferences. It also laid the foundation for future innovation including AI/LLM integration and advanced analytics.