Machine Learning solutions for forward-thinking businesses

As a Machine Learning software company, Miquido assists clients in enhancing products and making business decisions using state-of-the-art Machine Learning models

Professional software development and ML services

As a Machine Learning development company, we combine our expertise with software development, data analytics, visualisation and consulting support to ensure efficiency, safety and assistance to deliver end-to-end solutions. We create Machine Learning solutions using supervised, unsupervised and reinforcement learning to help you optimise your processes and enhance your products.

What we do – check out our Machine Learning services

Predictive Analytics

Predictive Analytics allows you to anticipate the future and make business decisions based on your current or historical data. It offers a variety of applications specific to a wide range of business sectors. Credit scoring can be applied to assess the likelihood of clients repaying loans. Sales forecasting is used to assess the demand for a product, while anomaly detection helps to identify risks and unexpected events with the use of data mining techniques. With the aid of predictive analytics, you can reduce risks while simultaneously improving business operations.

Churn Prediction

This is a part of predictive analytics that helps to answer a specific question: which customers end their relationship with a company (or stop using the product) and why? In other words, churn prediction lets you pinpoint when users are about to stop using your services before they do so. It becomes a massive asset when it comes to increasing customer retention. Thanks to churn prediction, you can learn what are the pain points related to your product or services, and find the right way to improve them.

Customer Analytics

Learning user behaviour and needs is crucial when it comes to digital businesses. Customer Analytics combines predictive analytics and customer segmentation to improve communication with customers and increase profitability as a result. We make it possible for companies to gain insights about the needs of a specific client segment and target tailored, direct marketing to their customers. When your selected user base is given the right message at the right time, you’ll see your conversion rates grow steadily.

Text Analytics

Text Analytics is used to translate large amounts of unstructured text into machine interpretable, quantitative data to speed up and automate all text-based processes. We can apply natural language processing methods to help you with all kinds of text, including documents, social media posts, surveys and chatbot conversations. One of the most popular applications of Text Analytics is automatic topic detection and sentiment analysis, which is particularly helpful when it comes to understanding your userbase’s needs.

Recommendation Systems

Personalisation is the key to success in the digital landscape, no matter which industry you operate in. Recommendation Systems powered by machine learning are used to predict user preferences based on their behaviour and experience. We apply recommendation engines to provide your customers or users with personalised content by suggesting the products and services they’re most interested in. By giving your users recommendations that are tailored to them, you can make sure to improve their satisfaction with the service, and increase overall sales.

Artificial Neural Networks (ANN) and Deep Learning (DL)

We harness most of the available Artificial Intelligence options to give you a solution you can be truly satisfied with. Neural Network-based solutions can find complex patterns in data that would otherwise remain hidden. We apply Artificial Neural Networks and Deep Learning solutions for image, character and speech recognition, where other Machine Learning methods are not applicable or efficient enough, in order to provide you with a seamless digital product that will leave your competitors far behind.

Tangible results, right on schedule

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Why should you use Machine Learning in your business?

Here are a few examples of how Machine Learning engineering can leverage cutting-edge techniques to help you solve complex business challenges and stay ahead of the competitors

Boosted sales revenue

The use of Machine Learning solutions can keep your conversion rates and sales growing while also reducing costs at the same time. Increase revenue results thanks to a better understanding of shopper needs, a more personalised user experience and effective marketing campaigns that make for satisfied customers.

Reduced operating costs

By limiting time-consuming human involvement with tasks that can be automated, operating expenses can be significantly reduced. Additionally, machine learning solutions help to detect unusual or unauthorised bank transactions, as well as identify and limit fraudulent insurance claims and loan applications that can put company finances at risk.

Increased operations speed

Automation of administrative tasks and client management systems reduce staff involvement. More effective customer service, detection of fake news, preventing cyberbullying and removing offensive comments in social media can be done using text analytics for instant and deepened interactions with customers and users.

Exploring unexploited areas

Harness the power of Machine Learning in a way that will benefit your business the most. Face detection and recognition, as well as attracting video-on-demand viewers based on their personalised interests are some examples of possibilities that can be brought to your company with the implementation of Machine Learning solutions.

Ready to create your ML Solution? Contact us!

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Where does Machine Learning excel?

Wherever there is enough data available along with a business justification, Machine Learning automatically translates different data types into information and actionable knowledge to support your business management.

  • Image Classification and Tagging
  • Natural Language Understanding
  • Speech Recognition
  • Speech to Text Conversion
  • Fraud Detection
  • Customer Retention
  • Recommendation Engines
  • Churn Prediction
  • Targeted Marketing
  • Processes automation
  • IoT – Internet of Things
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years in remote
software development
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digital solutions
delivered
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conducted remotely

Machine Learning applications at Miquido

Our tech stack

Data Processing

Python

Apache Spark

AWS Glue

Amazon EMR

Amazon Kinesis

Data storage

BigQuery

Delta Lake

PostgreSQL

Machine Learning

Amazon SageMaker

TensorFlow

Keras

Pandas

NLP

Tesseract

Visualisation

Google Data Studio

Power BI

Our development process

  • 1

    Scoping and Estimation

  • 2

    Workshops & Preparation

  • 3

    Design and Development

  • 4

    Product Release

  • 5

    Maintenance and Support

Understanding the nature of your project

First we identify the scope of your project and take our time to understand your requirements, business plans and expectations. We talk through the features you want your ML solution to have and the complexity of the entire project. This allows us to help you choose suitable tech services required to support your idea and estimate the development time. As a result of this phase, we give you a general quotation and development schedule.

Shaping the vision and discussing the details

Help us understand your business needs during Workshops with our Data Scientists and Data Engineers! During the meeting we will discuss all your requirements as well as available Machine Learning techniques, software packages and data infrastructure to help you choose the best strategy or sharpen your vision.

Bringing your project idea into reality

We start with data collection, consolidation and processing to get your data correctly formatted and ready for upcoming modeling. Then we select an algorithm suitable for your modelling case. After that, we let machines do their job! The chosen algorithm is now used to train the model using your data. Our development process is iterative as we refine and repeat modelling to make sure that the proposed model is the best for your specific case. The length of this phase depends on the project size and complexity and usually takes between 2-3 months.

Introducing your product to the market

As soon as your product is good to go, we take care of the deployment and release it under the agreed infrastructure. Timing is crucial here – we make sure that every element of the system is released on schedule and works perfectly. And even after the release, you can still count on our support and maintenance.

Taking good care of your product

It’s not a problem if you decide that your solution needs extra features or changes. We start working right away, all the while supporting the existing version. However, if you decide you want to transfer the project to your in-house team, we help you plan the process and make sure it goes smoothly.

Custom Machine Learning solution development

Are you struggling to find a solution to solve your complex business questions? Choose our custom AI-based services. We combine Machine Learning software development with data analytics, visualisation and consulting support to provide you with comprehensive insights.

What is there to gain? By exploring unexploited or previously unavailable analytical areas, we can help you to boost your business performance and stay ahead of the competitors. We will provide you with a complete solution, from assistance in goal setting and refinement all the way to visualisation and reporting solutions. Our fit-for-purpose approach leverages state-of-the-art methods and adapts them to make sure they do exactly what you need.

Read more about custom app development

Want to know more about Machine Learning?

Does Machine Learning sound confusing to you? Don’t worry, pick a question and we will provide you with a brief answer!

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How does Machine Learning work?

Machine Learning (ML) automatically recognises complex, previously unknown and useful information in all types of data. In the ML process, a model learns by looking for patterns hidden within given data. The more data there is, the more accurately the model resembles the real process. Additionally, by adjusting model parameters we can further improve its performance. Having an adequate model built, we can then generalise its application and make predictions about fresh data.

What is Machine Learning used for?

There are two common types of ML tasks. Firstly, classification that predicts output based on given data. This can be used, for example, for a credit scoring or product dement prediction. The second one type clustering that assigns similar objects together, which makes it a perfect solution for customer or user segmentation.

Curious about the details? Check our AI guide for business owners!

What is a Machine Learning lifecycle?

This is an iterative process of building Machine Learning solutions in a pipeline. Generally speaking, it consists of the following steps: data wrangling, model building and making predictions. The model is a simplified representation of a modeled process. The data phase includes data collection, data exploration, feature engineering, and splitting data into training and evaluation sets. After that, models are built, evaluated, and their performance is improved by hyperparameter tuning. With the model deployed, we can make predictions using new data. The last phase in the project lifecycle is model maintenance and support.

What is Feature Engineering?

Features or variables are measurable characteristics of the object (like a product or a customer) and are a basic input building block of all datasets. Based on them, prediction or clustering is performed. During the feature engineering process, new features can be obtained from raw features with the aim to improve an algorithm’s performance.

What is Natural Language Processing?

Natural Language Processing (NLP) is an automatic manipulation and understanding of written or spoken text. It intersects such fields as linguistics, artificial intelligence and machine learning.

How does a recommendation system work?

Recommendation Systems are algorithms used for suggesting relevant content or products for users or shoppers. Recommendations are generated on user-item attribute similarities.

What is Predictive Analytics?

Predictive Analytics models in business applications use patterns found in historical data to identify future risks and opportunities. Apart from machine learning techniques, predictive analytics also encompasses statistics and data mining. One of its well-known applications is credit scoring used in the financial sector.

What is Deep Learning?

Deep Learning (DL) relates to a subgroup of Artificial Neural Networks (ANN) that have more than 3 layers and therefore can extract higher-level features. ANN are a part of Artificial Intelligence that solve complex and non-linear problems by mimicking animal neuron network behaviour. DL systems are self-teaching and able to filter information through multiple hidden layers in order to resemble human brain processes with an even higher accuracy. It is an implementation of artificial intelligence and can be used e.g. for automatic machine translation, image classification, voice recognition or self-driving cars.

What is a cloud-based Machine Learning or Machine Learning as a service?

Cloud-based modelling merges Machine Learning capabilities with cloud-based computing environments. Cloud-based modelling can be performed using AWS SageMaker, Microsoft Azure, Google Cloud ML Engine or IBM Watson.

What are the limitations of Machine Learning?

Although ML can be applied almost everywhere, there are some limitations we have to be aware of. It requires a large amount of high-quality data to perform well and deliver reliable solutions. There is always some bias as we are working only on an available subset of the data that might not fully represent the modelled process. There is also an ethical dilemma with a responsibility for the outcome of ML-based decisions (e.g. a self-driving car accident). In some cases, a simple interpretability of modelling outcomes may not be possible.

What industries can use Machine Learning?

  • Healthcare
  • E-commerce
  • Entertainment
  • Fintech
  • Others

For our healthcare clients and care providers we offer patient risk identification and virtual assistance. By applying computer vision solutions, we can also assist you with medical image classification and tagging. Manual data entry can be replaced by automated, less error-prone processes.

See our solutions for healthcare

Product recommendation, customer churn prevention, price optimisation, demand response management—these are just a few things we can do for your business! We also excel in conversational AI and chatbots for automated customer service.

See our solutions for e-commerce

Thanks to our experience with content recommendation systems implemented for music streaming services, we are experts when it comes to boosting endorsement accuracy. Dedicated social media solutions may find their application in post management and analysis. By using NLP we can interpret user emotions and opinions (sentiment analysis), detect fake news and delete offensive comments.

See our solutions for entertainment

Our experience in the fintech industry can help your business make better credit scoring predictions. We can assist you with product design and development, portfolio management and pricing automation. Additional risks-related solutions may include fraud and unusual transaction detection as well as fraudulent insurance claims and loan applications.

See our solutions for fintech

Dynamic pricing, automated customer service, optimal marketing and product development; these are just a few more examples of potential benefits that Machine Learning solutions can bring into your business.

See our solutions for other industries

Check our blog for more insights

Want to talk about your idea?

Hi, I’m Jerzy, Head of Innovation & Business Development at Miquido. Fill in the form to the right and we’ll get in touch soon!


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