Turning test Data In

a knowledge mining & generation company dedicated to improve understanding data for enterprises and scientist, accelerating the application development and uncovering new connections . aaaaaaa

Agenda una de

Trusted by:

From Complexity To Ground Truth

What UDA brings to the table

UDA converts unassociated transactions into client-linked payments, automating 70% of manual identification work for 10,000+ transactions

OCR + multimodal extraction tuned for real‑world forms, statements, and remittances.

Automating loan payment matching through knowledge graphs and agent systems that deeply understand financial contexts and data rules.

Delivers intelligent client recommendations through AI and business knowledge graphs to empower manual identification processes.

Qnowledge

How are concepts connected in Wikipedia? Can you find the shortest path between two ideas? This interactive game challenges you to link two nodes concepts using the minimum number of Wikipedia pages possible. Want to give it a try?

Coming Soon

Complexometrum

Want to know if your dataset is Quantum AI-ready? Complexometrum analyzes data complexity and identifies opportunities for quantum-enhanced machine learning

Any Dataset to knowledge extraction

Ungraph

Turn any files—PDFs, reports, emails—into connected knowledge graphs. Give your data structure and relationships so your AI truly understands business context and provides more accurate answers.

pip install ungraph

Measurable Impact. Real Valu

Credit provider

Because Dominican banks data is inconsistent and lacks client IDs on deposits, Zenziya's entire credit follow-up process was manual. Employees were trapped in 8-hour daily cycles cross-referencing disconnected sheets to identify clients—an unsustainable bottleneck for growth.

What UDA Achieved

2452

Montly Transactions identified

10%

Automation ratea

0.6%

Error rate automatic identification

Research & Breakthroughsaa

17 jun 2025

Q2SAR: A Quantum Multiple Kernel Learning Approach for Drug Discovery

Alejandro Giraldo, Daniel Ruiz, Mariano Caruso , Javier Mancilla , Guido Bellomo

Quantitative Structure-Activity Relationship (QSAR) modeling is a cornerstone of computational drug discovery. This research demonstrates the successful application of a Quantum Multiple Kernel Learning (QMKL) framework to enhance QSAR classification.

6 May 2025

Quantum QSAR for drug discovery

Alejandro Giraldo, Daniel Ruiz, Mariano Caruso, Guido Bellomo

Quantitative Structure-Activity Relationship (QSAR) modeling is key in drug discovery, but classical methods face limitations when handling high-dimensional data and capturing complex molecular interactions. This research proposes enhancing QSAR techniques through Quantum Support Vector Machines (QSVMs).