No-code Artificial Intelligence tailored to your business needs

Dedomena brings the fastest, most effective and highest ROI approach to enterprise AI by combining pre-built AI solutions with embedded synthetic data and anonymization capabilities. Train, deploy and monitor best-in-class machine learning models, and unlock the value of your data.

Privacy by design

Test and fine-tune your models with embedded privacy-preserving synthetic data capabilities.

Superior performance

Train your models with large synthetic copies of your data and monitor performance by incorporating feedback from systems and users.

Model-driven approach

We have already built and tested the models you need, you just have to integrate them or fine-tune them with synthetic copies of your data.

Deliver AI anywhere, anytime

Deploy and scale a tested AI application through our API and deliver real-time insights into your system, users and customers.


Our mission is to accelerate the development and adoption of Artificial Intelligence

Step 1
Select your use case

Browse AI applets based on your business needs, goals and expected outcome.

Step 2
Test and fine-tune

Test and safely fine-tune your models with tons of synthetic copies of your original data.

Step 3
Put your model to work

Easily integrate your trained model into production environments and start delivering great value to your customers.

Step 4
Manage performance

Monitor accuracy and incorporate feedback into the model for an incredible performance.


Accelerate delivery and reduce the complexities of developing Artificial Intelligence.

Scale AI for your business

Use pre-built AI applications and streamline your data operations with industry-specific frameworks.

Deliver results in days

Rapidly deploy AI applications and capabilities in days and see results in weeks rather than months or even years.

Unlock meaningful value

Extract the value of your data and take a competitive advantage from reduced costs and increased revenue.

Built AI with confidence

Ensure data privacy with embedded anonymization and synthetic data generation capabilities.

Reduce costs

Businesses of every size can now take advantage of implementing Artificial Intelligence from a fraction of the cost.

Increased productivity

Encourage developers and experts to create, innovate, and become actively involved in organisational strategy thanks to its flexibility and ease of use.


Dedomena AI technology has been designed to improve AI performance across industries and purposes.

  • Banking
  • Health

Customer Scoring

Evaluate customers based on financial behaviour.

Fraud Detection

Pinpoint fraud risks for timely action to prevent revenue loss and fraud-related costs.

Transaction Enrichment

Understand customer´s transactionability with purchase and income categories, and merchant information.

Credit Approval

Pre-qualify low-risk candidates using AI models and expedite their approvals.

AML Prevention

Transform financial crime detection with AI.

Finance Forecasting

Accurately forecast revenue, balances with AI that identifies risks and opportunities.

Medical Records Data Extraction

Extract data from electronic health records (EHRs) and automatically turn it into usable, computer-addressable information.

Automatic Diagnosis & Disease Detection

Machine-learning-based disease diagnosis for predictive and effective treatment.

Simple Concepts Extraction

Extract clinical concepts from electronic health records into actionable knowledge.

Data Interoperability Standardization

Enable underlying exchange and sharing of clinical information in industry standards.

Medical Data Structurer

Structure patient records making research reliable, reproducible and efficient.


Check out some of our explanatory articles or cross-industry
use cases to know more

What is Synthetic Data and Why is it so important?

Without any doubt, synthetic data is one of the key technologies in the present and future of data science and artificial intelligence, but what is synthetic data? How do they work? And why is it key for data-driven companies?

The Benefits of Synthetic Data

The applications and benefits that synthetic data can bring in the development of data-centric strategies for companies are huge and growing in relevance for data science teams.

Anonymization & Data Sharing

Throughout this article, we will work with a Kaggle dataset, making a brief demonstration of how synthetic data is generated and how it preserves the statistical properties of the original data.

Why do traditional data anonymization methods no longer work?

The techniques that have always been used to anonymize data do not have the guarantee of ensuring the total anonymity of the data. These are techniques that have flaws when it comes to ensuring data privacy and utility.

Synthetic Data use cases in Financial Industry

The banking industry faces a wide variety of challenges when it comes to anonymization and data privacy. Thanks to synthetic data, banks can tackle data challenges more effectively and unlock the full potential of their data assets.

The Value of Synthetic Data in Financial Services

Data anonymization is key to applying the best data-driven strategies in the banking industry. The correct implementation of this strategy based on synthetic data will allow extracting all the knowledge and insights from customer financial data.

The Potential of Synthetic Data in Healthcare

Synthetic data has received considerable attention as a method of protecting patient privacy, diversifying datasets and augmenting clinical research, hence accelerating the application of Artificial Intelligence across different hospitals and institutions.

Synthetic Data for People Analytics

Companies now have access to more employee data than ever before, and, as data collection becomes easier, it is more important than ever for HR to take position on ethical data use, privacy and security.

Synthetic Data for insurance

Synthetic Data can help insurance companies to improve their underwritting, claims management and customer experience while unlocking the potential of data that have been hidden by data privacy regulations.