Entity resolution services for matching, deduplicating, and linking customer, account, business, and operational records across fragmented data sources.

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15+

Years building production software

120+

Engineers across delivery pods

75+

Clients served in North America

3K+

Successful product releases

Why Logiciel

Why Identity Matching Gets Harder as Data Scales.

Why Logiciel · 01

The same customer, company, or account can appear differently across CRM, ERP, billing, support, product, and external data sources.

Why Logiciel · 02

Names, addresses, emails, identifiers, and other attributes are often incomplete, inconsistent, outdated, or entered in different formats.

Why Logiciel · 03

Exact-match rules miss records that clearly refer to the same entity but contain spelling variations, abbreviations, or missing fields.

Why Logiciel · 04

Loose matching creates the opposite problem by incorrectly merging records that belong to different people or organizations.

Why Logiciel · 05

Duplicate identities distort reporting, segmentation, analytics, risk models, personalization, and downstream automation.

Why Logiciel · 06

Entity relationships become more difficult to resolve as datasets grow across systems, geographies, and business units.

Why Logiciel · 07

Teams need explainable matching logic with confidence thresholds and review controls, not a black-box merge process.

What you get

What You Get From Logiciel Entity Resolution Services.

We combine data engineering, deterministic matching, probabilistic methods, machine learning, and domain-specific rules to create more reliable entity identities across fragmented datasets.

A unified entity view

linking records that refer to the same customer, organization, account, asset, or other business entity

Better duplicate detection

identifying records that exact-match rules miss because of formatting differences, errors, aliases, or incomplete data

Confidence-based identity matching

using multiple attributes and evidence to determine how strongly two or more records belong together

Controlled entity merging

with thresholds, survivorship rules, exception handling, and review paths for uncertain matches

Cleaner downstream data

for analytics, customer intelligence, fraud detection, reporting, personalization, and operational workflows

Traceable matching decisions

with visibility into the attributes, rules, and signals contributing to entity resolution outcomes

An identity foundation that scales

as data volumes, sources, entity types, and matching requirements continue to grow

What we build

Entity Resolution Across Critical Data Workflows.

Customer Identity Resolution

What it meansMatch customer records across CRM, ecommerce, support, billing, product, marketing, and other systems to reduce fragmented profiles.

Business and Organization Matching

What it meansIdentify when company records with different names, domains, addresses, subsidiaries, or identifiers refer to the same organization.

Account and Profile Deduplication

What it meansDetect duplicate accounts, profiles, registrations, and records before they distort reporting or downstream processes.

Product and Catalog Entity Matching

What it meansConnect duplicate or inconsistent products, SKUs, suppliers, attributes, and catalog records across multiple datasets.

Asset and Operational Entity Resolution

What it meansMatch equipment, properties, locations, devices, or other operational entities represented differently across business systems.

Cross-Source Data Linking

What it meansLink internal and third-party datasets where identifiers are inconsistent, incomplete, or unavailable across sources.

Entity Resolution for Big Data

What it meansBuild scalable matching pipelines for large datasets where millions of potential record comparisons make simple pairwise matching impractical.

What we build

Entity Resolution Models Built Around Your Data Environment.

Dedicated Data Intelligence Squad

A cross-functional team works across data discovery, matching strategy, pipeline engineering, model development, validation, integration, and production rollout.

Entity Resolution Consulting and Team Extension

Data engineers, machine learning engineers, and solution specialists strengthen your team across identity matching, data quality, architecture, and implementation.

A focused initiative built around a defined problem such as customer deduplication, organization matching, cross-source identity linking, or master-data cleanup.

Under the hood

Entity Resolution Services We Deliver.

Entity and Data Source Discovery

We identify the entity types, source systems, identifiers, attributes, data-quality issues, duplicate patterns, and downstream outcomes that matter.

Included

Data Standardization and Preparation

We normalize names, addresses, contact details, identifiers, formats, abbreviations, and other attributes before matching begins.

Included

Deterministic Matching

We create high-confidence rules around exact identifiers and reliable attribute combinations where matches can be resolved predictably.

Included

Probabilistic and AI-Based Matching

We use similarity scoring, fuzzy matching, statistical techniques, embeddings, or machine learning where records cannot be resolved through exact rules alone.

Included

Entity Clustering and Golden Record Creation

We group records representing the same entity and apply survivorship rules to create a trusted representation without losing source lineage.

Included

Match Evaluation and Review Controls

We define confidence thresholds, precision and recall targets, ambiguous-match queues, validation datasets, and human-review paths where required.

Included

Production Pipelines and Monitoring

We integrate entity resolution into batch or streaming data pipelines and monitor match quality, data drift, exceptions, and changing source patterns.

Included

What we build

Entity Resolution Insights & Frameworks.

Entity Resolution Readiness Model

A practical framework for assessing source quality, identifiers, attribute coverage, duplicate patterns, data volume, and matching complexity before implementation.

What we build

Deterministic vs. Probabilistic Matching Framework

A structured way to decide when identity matching should rely on exact rules, fuzzy similarity, machine learning, or a combination of approaches.

What we build

Entity Match Confidence Model

A framework for balancing precision, recall, match thresholds, false merges, missed matches, review effort, and downstream business risk.

What we build

Our Entity Resolution Framework.

Entity and Business Problem Discovery

We identify which entities need to be resolved, where duplicate or fragmented records exist, and which business processes depend on better identity matching.

Data Profiling and Match Readiness

We assess source systems, identifiers, completeness, formatting, duplication patterns, attribute reliability, and representative edge cases.

Matching Architecture Design

We define normalization, blocking, candidate generation, deterministic rules, similarity scoring, models, thresholds, survivorship logic, and review workflows.

Build, Match, and Validate

We implement the entity resolution pipeline, test representative records, measure match quality, review false merges and missed matches, and refine the approach.

Deploy, Monitor, and Improve

We integrate resolved identities into downstream systems and monitor match performance as data sources, volumes, and entity patterns evolve.

Selected work

Tailored engineering for your industry.

Zeme · Real Estate Cut development costs 50% and launched 3× faster with dedicated dev teams.

Real Estate

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Leap · Construction Scaled to 7-figure ARR with AI-augmented software teams.

Construction

Scaled to 7-figure ARR with AI-augmented software teams.

KW · Real Estate 56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Real Estate

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Resources

Insights for tech leaders.

Framework · PDF

AI Governance Documentation Template

Framework · PDF

AI Security Vendor Scorecard

Framework · PDF

AI Governance Vendor Scorecard

In their words

What our clients say.

Teams that needed to ship fast, and did. Here's what partnering with Logiciel felt like from the inside.

I would highly recommend them to anyone looking to scale quickly or needing support in engineering, product, or QA.

Patrick Fingles

CEO, Leap

We don't just call them Logiciel; they're part of the Zeme team. Within the first week they were contributing meaningfully to our codebase.

Elior Alayev

Founder & CEO, Zeme

The Logiciel team worked tirelessly and built everything we needed, with security and best practices across our entire platform. It allowed us to become #1 in our industry, and we couldn't have done it without them.

David Buzzelli

Co-Founder, JobProgress

Questions

Frequently asked questions.

What is entity resolution?

Entity resolution is the process of identifying records across one or more datasets that refer to the same real-world entity, such as a person, company, account, product, property, or asset.

What problem does entity resolution solve?

The entity resolution problem occurs when the same entity appears in different systems with inconsistent, incomplete, duplicated, or differently formatted information. Entity resolution helps link those records into a more reliable identity.

What is an example of entity resolution?

A customer might appear as “Robert Smith” in a CRM, “Bob Smith” in a billing platform, and “R. Smith” in a support system. Entity resolution evaluates identifiers and other attributes to determine whether those records represent the same person.

How is entity resolution different from deduplication?

Deduplication usually focuses on finding duplicate records within a dataset. Entity resolution is broader and can identify the same entity across multiple datasets even when records are not exact duplicates.

How does AI help with entity resolution?

AI and machine learning can help compare complex or incomplete records, calculate similarity, identify patterns across attributes, and improve matching where deterministic rules alone are insufficient.

Can entity resolution work with very large datasets?

Yes. Entity resolution for big data typically uses techniques such as blocking, candidate generation, distributed processing, and scalable matching pipelines to avoid comparing every record against every other record.

How do you measure entity resolution accuracy?

Common measures include precision, recall, false-match rate, missed-match rate, confidence calibration, cluster quality, and performance on a validated set of known entity relationships.

Let's build

Turn Fragmented Records Into Trusted Identities.

Connect duplicate and inconsistent data across systems so analytics, AI, reporting, and operational workflows can work from a more reliable view of each entity.

Explore Your Entity Resolution Use Case

Entity Resolution & Identity Matching Services.

See Logiciel in action.

Why Identity Matching Gets Harder as Data Scales.

The same customer, company, or account can appear differently across CRM, ERP, billing, support, product, and external data sources.

Names, addresses, emails, identifiers, and other attributes are often incomplete, inconsistent, outdated, or entered in different formats.

Exact-match rules miss records that clearly refer to the same entity but contain spelling variations, abbreviations, or missing fields.

Loose matching creates the opposite problem by incorrectly merging records that belong to different people or organizations.

Duplicate identities distort reporting, segmentation, analytics, risk models, personalization, and downstream automation.

Entity relationships become more difficult to resolve as datasets grow across systems, geographies, and business units.

Teams need explainable matching logic with confidence thresholds and review controls, not a black-box merge process.

What You Get From Logiciel Entity Resolution Services.

A unified entity view

Better duplicate detection

Confidence-based identity matching

Controlled entity merging

Cleaner downstream data

Traceable matching decisions

An identity foundation that scales

Entity Resolution Across Critical Data Workflows.

Customer Identity Resolution

Business and Organization Matching

Account and Profile Deduplication

Product and Catalog Entity Matching

Asset and Operational Entity Resolution

Cross-Source Data Linking

Entity Resolution for Big Data

Entity Resolution Models Built Around Your Data Environment.

Dedicated Data Intelligence Squad

Entity Resolution Consulting and Team Extension

Entity Resolution Services We Deliver.

Entity and Data Source Discovery

Data Standardization and Preparation

Deterministic Matching

Probabilistic and AI-Based Matching

Entity Clustering and Golden Record Creation

Match Evaluation and Review Controls

Production Pipelines and Monitoring

Entity Resolution Insights & Frameworks.

Entity Resolution Readiness Model

Deterministic vs. Probabilistic Matching Framework

Entity Match Confidence Model

Our Entity Resolution Framework.

Entity and Business Problem Discovery

Data Profiling and Match Readiness

Matching Architecture Design

Build, Match, and Validate

Deploy, Monitor, and Improve

Tailored engineering for your industry.

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Scaled to 7-figure ARR with AI-augmented software teams.

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Insights for tech leaders.

AI Governance Documentation Template

AI Security Vendor Scorecard

AI Governance Vendor Scorecard

What our clients say.

Frequently asked questions.

Turn Fragmented Records Into Trusted Identities.