KnoDL. Radically Simple. Brutally Effective.

Tired of dirty data that stalls projects and corrupts analytics? We match millions of records from disparate sources with up to 98% accuracy in hours. Transparent, reliable, and without the "black box" of neural networks.

KnoDL by the Numbers

Results that speak for themselves.

98%

Matching Accuracy

Validated by manual review on enterprise-grade industrial data.

80%

Time Reduction

Free your specialists from manual data cleansing and correction tasks.

60M

Records in 17 Hours

Matching two different directories of 30 million records each.

2

Files to Start

Load your data "as is" without complex, time-consuming pre-processing.

Proven in Practice

Pilot projects demonstrate predictable performance, even on massive datasets. Hover over the chart for details.

Transparent Process, Real Results

No black boxes. See how KnoDL works with data—from a console command to the final JSON output.

bash

Click "Run Demo" to see KnoDL in action.

Result: matches.json


                        

Try KnoDL Right Now

Don't want to wait for a demo? Launch KnoDL locally in 5 minutes and test it on your own data.

Docker Image

Free version for testing and development. Limit: up to 100K records.

docker pull knodlang/kdlfree
Installation Guide

Commercial Pilot

Full-featured version on your data with KnoDL team support. No limits.

  • ✓ Any data volume
  • ✓ Integration support
  • ✓ SLA and delivery guarantees
Request Pilot

A commercial license is required for production use. The free version is intended for testing and development.

KnoDL vs Alternatives

A direct comparison across business-critical parameters.

Parameter KnoDL ML / Neural Nets ETL / Manual Work
Speed Hours / Days Weeks / Months Months / Years
Transparency 100% (Explainable algorithm) Low ("Black box") High
Resource Requirements Low (Runs on a laptop) High (GPU, Data Scientists) High (Engineering team)
Adaptability to Change Maximum (No retraining required) Low (Retraining required) Very low (Requires rewrites)

Who KnoDL Is For

We speak the same language as teams responsible for data quality, integration, and measurable outcomes.

Head of Data / CDO

For leaders building a strong data culture and owning data quality across the company.

Digital Transformation Leaders

For executives who need fast, reliable tools to accelerate transformation initiatives.

CIOs and IT Architects

For teams designing and supporting complex IT landscapes that require efficient integration.

Project Leaders

For managers delivering MDM, CRM, and integration projects where deadlines and outcomes matter.

Where KnoDL Delivers Maximum Value

Cleanse, Match, and Enrich Master Data

Go beyond finding duplicates. Enrich customer profiles and product catalogs by matching them with data from partners, suppliers, and external systems. Create a single, reliable source of truth.

The Future of KnoDL: From Core to Ecosystem

Our algorithm is a powerful core on top of which we build solutions that reshape the way organizations work with data.

KnoDL for Analysts

A visual low-code interface that helps business teams find duplicates and build clean data views without manual routine.

KnoDL for Data Engineers

SDK and API for automated deduplication and reference matching inside ETL and ELT pipelines.

KnoDL for Risk & Security

Entity correlation for anti-fraud, KYC, and AML with transparent rules and fully reproducible logic.

Solutions Ecosystem

Ready modules for MDM, master data, and integration in key industries from banking to manufacturing.

Frequently Asked Questions

Answers to key questions about deduplication, matching, and data provenance control.

A familiar problem: one customer can be recorded in CRM in multiple ways, which splits order history and distorts reporting.

KnoDL approach:

  • Automatic duplicate detection even when names, addresses, and entity labels are written differently.
  • Each duplicate pair gets a similarity score, so you see confidence level for every match.
  • Full transparency: for every pair, the system shows original records to simplify final merge decisions on your side.
  • Practical effect: tasks that usually take weeks of manual reconciliation are completed in hours.