Using large language models to process and analyze your text data, create chatbots, and generate content
Language Models (RAG)
Models that help you find the right customers based on your database
Look-a-like models
Models that generate or analyze your audio files
Speech Analytics (ASR, TTS)
Models that predict the behavior of your customers or events
Scoring models
Made projects
100+
Models that allow you to detect objects in photos and videos and analyze their behavior
Photo and video analytics
Our Products
7
CI/CD, Docker, Kubernetes
Deploy
6
5 Ways to check the quality and reliability of a model before Launching it
Validation
8
Visualization of results and quality control: PowerBI, Redash, etc.
Reporting
5
Creating different types of models using various, including neural network tools
Modelling
2
Methods: Checking for data completeness, Checking for consistency, Checking
for data validity, Checking for Data relevance, Checking for Data correctness, Checking for data uniqueness Tools: python, pandas, Dask, Modin, PySpark
for data validity, Checking for Data relevance, Checking for Data correctness, Checking for data uniqueness Tools: python, pandas, Dask, Modin, PySpark
Data Quality
3
Methods: generation of synthetic variables, agrigation, coding, normalization, smoothing
Features Engineering
4
Methods: Checking the information significance and stability of features, dimensionality reduction, regularization
Features Selection
Data Collation /
Data Warehouse
Data Warehouse
Tools: Apache Airflow, Clickhouse, Postgresql, MongoDB, S3, Rabbit, Kafka, Google BigQuery, BeautifulSoup, RESTful API
1
From idea to implementation, our development process goes through the following stages of work and the application of methods and tools:
Advanced technology
Data Collation / Data Warehouse
Инструменты: Apache Airflow, Clickhouse, Postgresql, MongoDB, S3, Rabbit, Kafka, Google BigQuery, BeautifulSoup, RESTful API
Инструменты: Apache Airflow, Clickhouse, Postgresql, MongoDB, S3, Rabbit, Kafka, Google BigQuery, BeautifulSoup, RESTful API
Инструменты: Apache Airflow, Clickhouse, Postgresql, MongoDB, S3, Rabbit, Kafka, Google BigQuery, BeautifulSoup, RESTful API
Data Quality
Методы: Проверка на полноту данных,
Проверка на консистентность, Проверка
на достоверность данных, Проверка на актуальность данных, Проверка на корректность данных, Проверка на уникальность данных Инструменты: python, pandas, Dask, Modin, PySpark
Проверка на консистентность, Проверка
на достоверность данных, Проверка на актуальность данных, Проверка на корректность данных, Проверка на уникальность данных Инструменты: python, pandas, Dask, Modin, PySpark
Features Engineering
Методы: генерация синтетических переменных, агригация, кодирование, нормализация, сглаживание
Features Engineering
Методы: генерация синтетических переменных, агригация, кодирование, нормализация, сглаживание
Features Engineering
Методы: генерация синтетических переменных, агригация, кодирование, нормализация, сглаживание
Features Engineering
Методы: генерация синтетических переменных, агригация, кодирование, нормализация, сглаживание
Features Engineering
Методы: генерация синтетических переменных, агригация, кодирование, нормализация, сглаживание
Features Engineering
Методы: генерация синтетических переменных, агригация, кодирование, нормализация, сглаживание
We have doubled the speed of creating a customer card in CRM, reducing the number of manual typos.
They completed checklists using artificial intelligence, increasing the assessment speed from 30 minutes to 1 minute.
We have optimized the process of drawing up a sales plan from 1 week to 1 day.
Conversion rate increased by 12% after feedback
to sales managers
They completed checklists using artificial intelligence, increasing the assessment speed from 30 minutes to 1 minute.
We have optimized the process of drawing up a sales plan from 1 week to 1 day.
Conversion rate increased by 12% after feedback
to sales managers
We have reduced the speed of checking students' homework from hours to minutes.
We have created a system of checklists, according to which the AI Assistant evaluates the quality of the work performed.
We increased student engagement thanks to recommendations, and the timeliness of assignments increased by 18%.
We have created a system of checklists, according to which the AI Assistant evaluates the quality of the work performed.
We increased student engagement thanks to recommendations, and the timeliness of assignments increased by 18%.
Document recognition
Evaluating the quality of a call center's work based on checklists
Forecasting the purchase of real estate
Dynamic pricing
Development
Description:
We have developed models that allow us to sell apartments at the most optimal market value, create a monthly and quarterly sales plan, and improve the speed and quality of customer service through an AI Assistant.
The accuracy of the forecast for SKU exceeded 82%.
Minimizing the shortage and excess of goods on the market.
Increased marginality by 4% due to dynamic pricing.
Minimizing the shortage and excess of goods on the market.
Increased marginality by 4% due to dynamic pricing.
The forecast accuracy is 87%.
Key markers
of cancer risk have been identified.
The possibility of preventive response and
informed planning of screening
Key markers
of cancer risk have been identified.
The possibility of preventive response and
informed planning of screening
Retail
Description:
Building a short-term sales forecast based on sales history.
Price optimization based on demand elasticity.
Price optimization based on demand elasticity.
Sales forecasting model
Intelligent pricing
Predicting cancer risks
Screening
Predictive analytics
Pharmacology
The analysis of 214 medical confessions in 3200 patients and the construction of a pattern detection model.
Description:
Our cases
We have implemented more than 100 projects on various topics for our clients
90+
Recommendations
An AI Assistant for checking homework
Recommendations for students
We have developed models that allow us to evaluate students' work, give grades, and make recommendations on learning.
Education
Description:
Assessment of the quality of work based on checklists
Описания кейса Кредит
Кредит — это финансовый инструмент, позволяющий людям и компаниям получать средства для реализации своих целей. Правильное использование кредита может помочь достичь мечты, но важно помнить о рисках и обязательствах.
Кредит — это финансовый инструмент, позволяющий людям и компаниям получать средства для реализации своих целей. Правильное использование кредита может помочь достичь мечты, но важно помнить о рисках и обязательствах.
Кредит — это финансовый инструмент, позволяющий людям и компаниям получать средства для реализации своих целей. Правильное использование кредита может помочь достичь мечты, но важно помнить о рисках и обязательствах.
Описания кейса Девелопмент
Кредит — это финансовый инструмент, позволяющий людям и компаниям получать средства для реализации своих целей. Правильное использование кредита может помочь достичь мечты, но важно помнить о рисках и обязательствах.
Кредит — это финансовый инструмент, позволяющий людям и компаниям получать средства для реализации своих целей. Правильное использование кредита может помочь достичь мечты, но важно помнить о рисках и обязательствах.
Кредит — это финансовый инструмент, позволяющий людям и компаниям получать средства для реализации своих целей. Правильное использование кредита может помочь достичь мечты, но важно помнить о рисках и обязательствах.
MVP in the first
3 months
3 months
About us
+ 13 years
of ML experience
of ML experience
We work on projects in various industries, including finance, healthcare and retail, ensuring the implementation of AI that really brings results.
We combine the long-term experience of experts and modern machine learning algorithms to create solutions that help businesses automate processes.
10 years
We work in the market
100+
Completed projects
Guaranteed results
Our partners who support:
