5%

95%

the number of successful flights has increased

by

to

the accuracy of flight success forecasting has increased

A system for predicting the success of construction waste removal missions was created to improve process management and reduce financial losses.

Predicting the success of construction waste removal flights at the time of flight launch

системы на базе ИИ

7%

5%

the accuracy of gender prediction has increased

by

by

the accuracy of age prediction has increased

We developed an algorithm to improve the accuracy of gender and age prediction based on responses from various vendors.

Compilation of vendor expert assessments for predicting gender and age from CCTV cameras

системы на базе ИИ

6

3%

planning the purchase of server equipment

the risk of server failure has been reduced

by

months in advance

A server load forecasting system has been developed to optimize resources and prevent peak loads, allowing for efficient IT infrastructure management.

Server Load Prediction System: Improving the Efficiency of Computing Resource Management

системы на базе ИИ

6-9

IT budget planning and tender procedures

months in advance

How to improve the efficiency of software procurement through IT budget planning and tender procedures

Software License Usage Forecasting System: Reduce Costs and Downtime

системы на базе ИИ

30%

to

reducing costs for purchased software licenses

20%

10%

planning efficiency has increased

by

by

the number of overdue schedules has decreased

How a prototype schedule forecasting system helps proactively address delays and improve production processes

A study on forecasting the dynamics of production schedules for increasing production efficiency

системы на базе ИИ

automation of routine tasks and labor-intensive processes

forecasting of production resources, schedules, etc.

forecasting and analysis of customer behavior and industry trends

personalized solutions to improve customer service

Business values

about the service

AI-based systems development framework

Survey and data collection

Model development

Implementation
of the model

Support

Survey and data collection

01/04

Data, business, and production process analysis

Formation of applied hypotheses

Assistance in building a process for collecting, enriching, normalizing, and updating the necessary data for training ML models

Definition of data storage model and processing algorithms

Model development

02/04

Selecting the optimal ML model

Creation of new proprietary mathematical models for complex non-stationary dynamic processes

AI model training, testing, and validation

Implementation of the model

03/04

Integration of ML models into internal data update systems

Testing on different data sets

Setting up model monitoring and various quality metrics

Deploying the model in an industrial environment

Support

04/04

Updating new versions of the model

Training of on-site employees on how to work with the ML model

Technical support, troubleshooting and problem solving

the ability to dynamically calculate the best price for a service

Personalization of tariffs

Based on analytical data: demand, adaptability, prices and offers of competitors, historical data, etc., the ML model determines the optimal cost of the service

BUSINESS VALUES:

/01

maximize sales conversion and improve pricing accuracy

ensure service providers increase their revenue and customer loyalty

minimizing costs on inventory

Inventory demand forecast

Calculating product demand based on specific parameters, such as sales history, current prices, promotional offers, and sales geography

BUSINESS VALUES:

/02

increasing the availability of product range in retail outlets

inventory optimization

supplier performance assessment

encuring a high level of service

increasing sales margins

Pricing optimization

Based on data such as demand, adaptability, competitor prices, expiration dates, and historical data on purchase volumes, the ML model determines the optimal price for the product.

BUSINESS VALUES:

/03

minimizing costs for write-off and disposal of products

customer retention through personalized communications

Predicting customer churn

To predict which current customers will no longer use the product, the ML model compares the parameters of existing and departed customers, such as engagement, order history, age and gender characteristics, etc.

BUSINESS VALUES:

/04

improving the level of service

Identifying periods of high demand for delivery

Based on historical data on the number and size of orders, taking into account the geography of sales, the ML model predicts the most popular delivery times for orders and the required number of couriers

BUSINESS VALUES:

/05

expansion of territorial coverage, with high delivery speeds at any time

identify suspicious accounts with higher accuracy than traditional methods

Detecting fraudulent reviews and ratings on marketplaces

The ML model is trained on the activity history of current users, as well as users with blocked accounts, such as the number of comments, ratings, publications per day, registration date, etc.

BUSINESS VALUES:

/06

the ability to combat rating and review manipulation

increasing user trust

increase in the average bill

Planning promotions

The ML solution analyzes key parameters of previous promotions and predicts the results of future ones.

BUSINESS VALUES:

/07

reduction of inventory

increasing customer trust and loyalty

Delivery time forecast

Based on the company's historical data: delivery times for different types of goods, supplier information and location, and customer reviews

BUSINESS VALUES:

/08

based on external data: weather, seasonality, exchange rates

based on specified restrictions, for example, the number of couriers per shift

Increased revenue and customer loyalty through a high level of personalization

Recommender systems

The ML model predicts which product, service, or advertising offer will be of interest to the client.

BUSINESS VALUES:

/09

The solution takes into account customer data and product preferences: order history, average purchase order, engagement, etc., and compares it with data on other users to generate a forecast/

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