Artificial Intelligence & Machine Learning
Reveal the potential of your data with cutting-edge AI and ML technologies. Transform raw information into actionable insights, automate decision-making processes, and drive innovation across your business.
Our services
AI/ML Tool Selection and Integration
Evaluating and recommending AI/ML platforms, frameworks, and tools based on business needs. Integrating AI/ML solutions with existing IT infrastructure and customizing off-the-shelf AI/ML systems.
Predictive and Prescriptive Analytics
Developing predictive models to forecast trends, customer behavior, and market changes. Implementing prescriptive analytics to recommend actions based on predictive insights. Leveraging AI/ML to optimize decision-making processes.
AI-Powered Personalization
Implementing AI-driven personalization engines for marketing, sales, and customer engagement. Enhancing customer experiences through personalized interactions and offerings.
Natural Language Processing (NLP)
Implementing NLP solutions for text analysis, sentiment analysis, and language translation. Developing chatbots, virtual assistants, and conversational AI systems.
Model Development and Deployment
Designing, developing, and training custom AI and ML models tailored to specific business needs. Deploying models into production environments, ensuring they integrate seamlessly with existing systems.
Technologies
Our experience
Neural Network for Text Classification
Solution:
Using the historical transactional data in Food-related categories, to identify product categories which customer is most inclined to purchase in the future.
- Data processed and analytical model developed with use of Cloud Service Provider, Spark, Python and Open Source libraries.
- Features extracted from the unstructured data have been used in other models and allowed improving the accuracy of recommendations for the clients by 4% on average.
Requirements:
Results
78%
130
DataLab TCO Reduction
EDW, Teradata, SAS: TCO reduction
300k
Processed product items per hour
Solution:
Customer - Operator smart matching
Requirements:
Increase the conversion of via-call campaigns performed by out-bound CC operators through the use of accumulated statistical data on call results.
Solution:
- Implemented a method for distributing customers among operators to achieve a higher probability of sales (based on mathematical models).
- Clustered operators and clients by social and demographic features with account to their audio features with use of Cloud Service Provider, Docker, Python, Open Source libraries.
Results
+2%
+450k $
Response rate increase from 12 to 14%
NPV from additional sales per year
50k
Calls analyzed monthly
Next Best Offer reformation
Requirements:
Improve the Next Best Offer (NBO) algorithm to achieve sales KPIs and add new products in it.
Solution:
- New models were built and old propensity models were retrained.
- Implemented complex logic for the optimization algorithm considering all necessary configurable constraints: channel, product, contact policy, budget.
- Set up an optimization engine for a specific task.
Results
Over 5%
2x
More purchases across all bank products
Reduced time to add a new product to Next Best Offer process
3x
Faster Next Best Offer engine calculation
Personalization of cashback rate and category
Requirements:
Prepare the selection of proposals tailored to the individual needs of the client and aligned with the bank's strategy for developing customer behavior (addressing objectives related to growth, retention, and reactivation).
Solution:
- Automated preparation of Personalized offers by solving a complex optimization task, in which the forecasts of several mathematical models serve as dynamic constraints.
- The selection of proposals goes through a simulation assessment of the bonus budget distribution, which allows not to exceed the amount of cashback actually issued.
Results
+10%
Over 50%
Cashback payouts were increased for customers who valued cashback, reducing the bonus budget spent on those indifferent to it.
Cashback payouts have been reduced, while the total amount of POS turnover remains the same.
Automation of personal combo recommendations
Solution:
Increase revenue by personalizing and automating the mechanics of combo offers.
- A recommendation system has been developed and implemented
- It included rating of a dish and combo for each application user, along with a filter to control the KPIs objectives.
Requirements:
Results
+ 4%
Increased revenue, orders, reactivated customers
Solution:
Neural Network for Text Classification
Requirements
Using the historical transactional data in Food-related categories, to identify product categories which customer is most inclined to purchase in the future.
solution
- Data processed and analytical model developed with use of Cloud Service Provider, Spark, Python and Open Source libraries.
- Features extracted from the unstructured data have been used in other models and allowed improving the accuracy of recommendations for the clients by 4% on average.
results
78%
130
Accurate Fasttext multi-class classification model developed
Product items in Food category added to the built directory
300k
Of data loaded per hour
Customer - Operator smart matching
Requirements
Increase the conversion of via-call campaigns performed by out-bound CC operators through the use of accumulated statistical data on call results.
solution
- Implemented a method for distributing customers among operators to achieve a higher probability of sales (based on mathematical models).
- Clustered operators and clients by social and demographic features with account to their audio features with use of Cloud Service Provider, Docker, Python, Open Source libraries.
results
+2%
50k
Response rate increase from 12 to 14%
Calls analyzed monthly
+450k $
NPV from additional sales per year
Next Best Offer reformation
Requirements
Improve the Next Best Offer (NBO) algorithm to achieve sales KPIs and add new products in it.
solution
- New models were built and old propensity models were retrained.
- Implemented complex logic for the optimization algorithm considering all necessary configurable constraints: channel, product, contact policy, budget.
- Set up an optimization engine for a specific task.
results
2x
3x
Reduced time to add a new product to Next Best Offer process
Faster Next Best Offer engine calculation
Over 5%
More purchases across all bank products
Personalization of cashback rate and category
Requirements
Prepare the selection of proposals tailored to the individual needs of the client and aligned with the bank's strategy for developing customer behavior (addressing objectives related to growth, retention, and reactivation).
solution
- Automated preparation of Personalized offers by solving a complex optimization task, in which the forecasts of several mathematical models serve as dynamic constraints.
- The selection of proposals goes through a simulation assessment of the bonus budget distribution, which allows not to exceed the amount of cashback actually issued.
results
+10%
Cashback payouts were increased for customers who valued cashback, reducing the bonus budget spent on those indifferent to it.
Over 50%
Cashback payouts have been reduced, while the total amount of POS turnover remains the same.
Automation of personal combo recommendations
Requirements
Increase revenue by personalizing and automating the mechanics of combo offers.
solution
- A recommendation system has been developed and implemented.
- It included rating of a dish and combo for each application user, along with a filter to control the KPIs objectives.
results
+4%
Increased revenue, orders, reactivated customers
See more about services for your Data...
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