Machine Learning Development Company
DashBouquet is an experienced machine learning development company that provides custom solutions to businesses of all sizes worldwide.

Machine Learning Services

We help companies to get a clear understanding of the advantages of ML integration in each particular case and to overcome the obstacles on the way to implementation of the innovative technology.

What our clients say



Everyone, from their management team to their developers, is willing and ready to answer our questions.


Anna-Lena Gerber

We were so satisfied with the work of Dashbouquet that I recommended them to a lot of people in my network.


Rubporn Memoli

They are knowledgeable and quick-working, producing quality work.


Robert Fair

Dashbouquet is excellent, I would give them a perfect score.

Machine learning consulting services

Our experts enable companies to leverage the power of cutting-edge technologies by delivering machine learning consulting services. We empower businesses with our expertise through providing a variety of machine learning services.

DashBouquet has an excellent track record of creating custom machine learning solutions and delivering exceptional machine learning consulting services. Our seasoned developers provide top-notch machine learning services, from creating capable deep learning solutions to building stunning ML-driven mobile apps.

Machine learning algorithms are used in a variety of applications, such as:

Image recognition
Image recognition is one of the most common ML applications which is used by companies across various industries. It allows for instant personal identification, recognition of objects, and places.
Medical diagnosis
Predictions and insights delivered by ML-enabled software save time for medical specialists by facilitating faster decision-making.
Machine learning allows for optimization of the processes as ML-driven systems adapt in accordance with data that goes through them and via the feedback received. The optimization of distribution routes is one of the examples of effective ML implementation.
Predictive analytics
ML implementation allows for making accurate predictions using historical data while simultaneously learning from current observations. Machine learning algorithms are commonly used in forecasting demand for products, predicting possible changes in the market, and customer churn.
Recommendation services
Basic ML algorithms can predict user preferences thus aiding in delivering personalized recommendations and making more relevant suggestions.
Fraud detection
Flexible deep learning solutions allow uncovering subtle trends and catch fraud patterns for accurate detecting of fraudulent purchases or transactions.

Machine learning models

Mobile and web tech solutions with embedded machine learning have many uses in our everyday life. Machine learning can be used to solve complex business problems, gather meaningful insights, and make use of bulk unsorted data. And it’s high time for its implementation since the costs have fallen dramatically with the availability of cloud-based solutions.

Machine learning methods involve creating a model that undergoes training on some training set of data to be able to process additional data and make predictions. Common models include:
Artificial neural networks
Artificial neural networks interpret raw input by clustering and labeling data. Creating neural networks allow for recognizing patterns and understanding the context.
Decision trees
Decision tree learning is a predictive modeling approach that allows going from observations about an item to projections about its target value. This approach is used in machine learning to enable forecasting.
Regression analysis
Regression analysis models allow for forecasting a continuous outcome variable based on the values of several predictor variables. This technique is an important tool that finds relationships between the variables allowing for better modeling and analyzing data.
Deep learning
Deep learning is a subset of the neural network branch of machine learning that allows for more complex modeling. These algorithms process raw data and progressively extract higher-level features out of it enabling solving complicated tasks such as precise image or voice recognition. Deep learning models are based on multi-layer artificial neural network structures.