AI-Powered Data & Analytics

Understand performance, and generate insights

We help organizations use data to support better decision-making.

Analyze data across systems and sources
Identify patterns, trends, and drivers of performance
Build predictive models using machine learning to anticipate outcomes

Our approach is practical: focus on high-impact use cases and turn data into actionable outputs.

Applications

Data and analytics involve collecting, structuring, and analyzing data to generate insights and support decisions.

Beyond reporting, the focus is on understanding what is happening, why it is happening, and what is likely to happen next.

Performance analysis and reporting

Consolidation and analysis of data from multiple sources to understand business performance.

Centralized dashboards across systems
KPI tracking and monitoring
Identification of trends and anomalies

This provides a clear and consistent view of performance across the organization.

Data integration and structuring

Connecting and organizing data from different systems to make it usable and reliable.

Integration of CRM, ERP, marketing, and operational data
Data cleaning and standardization
Creation of structured datasets for analysis

This enables consistent analysis and avoids fragmented or unreliable data.

Predictive analytics and machine learning

Use of statistical models and machine learning to predict outcomes and support decision-making.

Demand forecasting
Customer behavior prediction
Lead scoring and prioritization
Risk detection and anomaly detection

These models help anticipate what is likely to happen and guide actions accordingly.

Decision support systems

Building systems that translate data into insights, recommendations, or actions.

Scenario analysis and forecasting
Identification of key drivers of performance
Automated insights and alerts

This allows teams to move from reactive analysis to more proactive decision-making.

How we work

We focus on turning data into usable insights and decision tools.

Data assessment

Understanding available data, structure, and quality

Use case definition

Identifying where analytics can drive impact

Data preparation

Cleaning, structuring, and integrating data sources

Model and analysis development

Building analytical models and machine learning solutions where relevant

Deployment and iteration

Integrating outputs into workflows and improving over time

Technologies we use

We work with modern data and analytics tools, integrated into the client’s existing architecture.

This includes:

Data warehouses and databases (BigQuery, Snowflake, etc.)
BI tools (Looker, Power BI, etc.)
Python-based analytics and machine learning models
Data pipelines and ETL processes
Monitoring and validation mechanisms for data and models

Technology is selected based on the use case, prioritizing reliability, scalability, and maintainability.

When AI-powered data & analytics make sense

Data exists but is fragmented across systems

Decisions rely heavily on intuition rather than data

There is limited visibility into performance drivers

Forecasting and planning are inaccurate or manual

Getting started

Data and analytics are not just about reporting, but about enabling better decisions across the organization.

At Liorant, we focus on building practical solutions that turn data into insights and actions.

If you are looking to better use your data, we can help you identify opportunities and implement solutions.