AI Development Services
AI Development Services we Provide
As a leading AI development company, we will show how many opportunities come from implementing AI into your business. Besides, we will help you determine and remove barriers to AI innovation.
Together we will look for the areas where this technology will be most beneficial to your business. Our AI development company will create and develop algorithms for you. We can also help to train AI services you built yourself and integrate models into the company’s workflow.
The team of seasoned professionals will deliver relevant and impactful custom AI software, which will help you outpace the competition. Our AI solutions company can build intelligent systems, performing tasks quicker and cheaper than humans, and doing their job anytime and anywhere.
No matter what industry you are in, your business will surely benefit from improved analytics, automation of routine tasks, and enhanced efficiency thanks to better processing of large datasets.
We will help you build smarter AI-based mobile apps, which serve business purposes better and boost revenue. Use its power to engage users, improve decision-making with AI-driven development and boost personalization.
In particular, AI can tell mobile developers how users interact with apps and what they are looking for. With the help of the AI development company for implementing this technology will help to provide better insights and suggest changes in applications for a better experience. Moreover, it can provide user-centric recommendations and messages.
Is your business ready for AI?
And how artificial intelligence service providers can help your business develop and scale?
Artificial Intelligence is no longer a thing from the movies and is far from being a fad. It has become a must-have for a successful business. Understanding the importance of working with data and employing algorithms can make a difference between leaders and stragglers.
Let’s make it clear — AI is not inherently “intelligent”, no matter how strange it sounds. At first, there are only “naked” algorithms or models that become intelligent after being trained on huge volumes of data. For most business applications, it means considerable amounts of company-specific data. And that is where AI software development services come in.
Training algorithms is not as easy as it may seem. To achieve the success you need:
- The large volume of high-quality data. The role of data in AI solution development is far more important than it is in advanced analytics and big data applications. Data collection and processing are the most time-consuming activities in this process.
- A well-developed system, combining information from various sources, training, and integrating findings from more recent data. Sometimes, the organization owns all required data, but they are fragmented across multiple systems. As a result, the process of training models is hindered. An artificial intelligence software development company will help your business to overcome those difficulties.
The need to get appropriate data for training AI algorithms has wide-ranging implications for the usual make-versus-buy dilemma of new technology investments. As you can see, getting value from AI is more complicated than just making or buying this technology for a business process.
Training AI algorithms requires various skills, including understanding how to build algorithms, how to collect and process data for training, and how to supervise the training. So to use the power of AI, you need a reliable Artificial Intelligence development company as your partner. And Data Science UA can offer you top-notch AI development services bringing the expertise of experienced and skillful AI engineers to your disposal.
What clients and partners say about us
Olga Shevchenko
CEO, EVA
Jake Diner
Founder and CEO, Elafris Inc
Oleg Bilozor
CEO and Founder, Reply
Michael Korkin, Ph.D.
CTO at Entropix, Inc.
AI development process
- 1
Problem
Problem definition and details discussion
- 2
Discovery & Documentation
Сollection of all necessary data, files, and documentation.
- 3
Timeline
Timeline definition and project details agreement.
- 4
Team Assembly
IT team organization to complete a project
- 5
Development
The initial phase of project actualization.
- 6
Deployment
Research, development, and implementation of the main part of the project by the IT team.
- 7
Post-project stage
Summing up and analysis of the results.
- 8
Support and maintenance
Further support for the developed product, if applicable.
Our Cases
AI R&D center for US product company
Together with American colleagues, our team creates a solution based on Computer Vision / Machine Learning.
Objectives:
– Reduce injury risks and prevent accidents in steel production with AI.
– Assemble a team of 10 talented engineers in a month amid quarantine.
Beauty and health stores chain (Ukraine)
The largest national retail chain of beauty and health stores, offering more than 30,000 assortment items.
Objectives:
– Up-sell and cross-sell enabling through a recommendation system.
– Clients churn prediction
Solar panels installer (Netherlands)
Rooftop solar panels installation for residential houses.
Objectives:
– Label roof coordinates and types based on satellite images (R&D project).
Odin money (US)
Odin is a global mobile banking app that offers keeping all your bank accounts in one place. Bills and financial milestones track through one integrated experience.
Objectives:
– Create and use ML model for the classification of all transactions.
Industries We Serve
1. Marketing
Marketing teams tend to have lots of data about advertising, web analytics, customer behavior, etc. We can fine-tune all data analysis solutions to run like clockwork and free up more of your marketing team’s time to be strategic and effective. Our data science services company uses machine learning to:
– forecast sales;
– recommend products;
– analyze assortment and so on.
2. Retail (E-commerce)
Retail usually accumulates large amounts of data and is eager to use data analytics.
We can help with:
– customer analysis;
– assortment analysis;
– sales forecasts;
– marketing and advertising budgets optimization;
– increase the efficiency of merchandising and supply chain management.
3. Manufacturing
Generation of optimized plans that enable predictive maintenance is one of the key goals for AI in manufacturing, as well it helps in:
– optimizing production lines and logistic chains;
– forecasting revenue;
– determining optimal employee workloads;
– setting up automated systems for monitoring compliance with safety regulations.
4. IoT
When artificial intelligence is working with IoT devices it means that data can be analyzed and decisions can be made without involvement by people. In a broad variety of industries where IoT is implemented, AI can help to identify patterns and detect anomalies in the data that smart devices and sensors transfer (for example, air quality, humidity, temperature, pressure, vibration, sound, and others).
5. FinTech
FinTech companies usually work with sensitive information and have high-security standards. We take all necessary precautions to keep their data safe. Data Science UA can assist such businesses in:
– credit scoring;
– recommendation systems for both new and prospective clients.
6. Logistics & Warehouses
The transportation and warehouse industry is data-driven and needs analysis of historical and real-time data performed by intelligent algorithms. So our team can help with:
- traffic management improvements
- warehouse optimization,
- route optimization (“travelling salesman” problem),
- developing optimal loading systems and utilization systems for vehicles;
7. Insuarance
AI can help insurance companies deliver high-quality service as it has done for major leaders in other industries such as Healthcare, Fintech, etc.
Our data science agency can help to:
- create a more personalized service;
- predict the repair costs from historical data;
- provide a selection of better investments based on risks, preferences, and spending patterns;
- improve claims analysis.
8. Agriculture
Farmers aim to maximize production and profits using innovative software and data collection and analysis. We can make the analysis of historical and real-time images & data collected from databases, satellites, drones, IoT sensors that can help to:
- increase the yield of farmlands;
- ensure serviceability of farm equipment;
- monitor fields conditions, irrigation, soil moisture, etc;
- predict weather conditions.
9. Cybersecurity
Nowadays AI helps to deploy effective cybersecurity technology and allows businesses to solve major cybersecurity challenges: cyberattack, financial loss, or brand reputation damage. We can help cybersecurity teams to:
- analyze patterns in user behaviors and respond to changing behavior;
- identify cyber vulnerabilities and irregularities in the network.
10. Healthcare
AI is already transforming the healthcare industry—helping patients and hospitals optimize costs and increase care delivery through actionable insights. We can help to:
- manage and analyze data to provide;
- improve preventive care;
- create personalized treatments;
- make optimization of scheduling and bed management;
- detect and analyze patient patterns and correlations for better decision making.
Our AI Development Tech Stack
Languages:
Visualization:
-
Power BI -
Tableau -
Qlik -
Matplotlib -
Seaborn -
Ggplot2 -
Plotly -
Bokeh
DBMS:
- Relational (MS SQL, PostgreSQL, MySQL)
- Non-relational (MongoDB, CouchDB, Cassandra etc.)
- Distributed (Hadoop etc.)
DL Frameworks:
-
PyTorch -
Tensorflow -
Keras
Architectures:
-
On-premise -
Cloud -
Hybrid
Algorithms:
-
Supervised learning (classification, regression) -
Unsupervised learning (clustering, dimensionality reduction, anomaly detection, pattern search) -
Ensembles -
Reinforcement learning
Fields:
-
Natural Language Processing -
Computer Vision -
Recommendation systems -
Tabular data analysis -
Signal Processing
Neural Networks:
- CNN
- RNN
- DNN
- LSTM
- GAN
- Autoencoders
Cloud Platforms:
-
Amazon Web Services -
Google Cloud Platform -
Microsoft Azure
Why Choosing Data Science UA?
We can successfully cooperate with various teams in your company to bring the most benefit for your business:
-
Discovery.
Such collaboration frequently begins with developing a proof of concept. Usually, stakeholders are people with little technical knowledge and “high-level” goals. Our company, as an Artificial Intelligence solution provider, can break down these goals into logical steps, identify and prioritize use cases, and offer the best solution for each.
-
Team Extension.
You may already have a data science team, but it’s hard to cover all possible needs in-house. We can be a valuable asset, boosting your expertise in certain subfields of AI, such as natural language processing, computer vision, and predictive analytics;
-
AI solution adaptation.
Machine learning may not be the key expertise of your company. We can join forces with your engineering team, providing the API of an ML system that fully suits your needs. Thus, your specialists can devote their time and efforts to the primary tasks rather than try to master an entirely new discipline;
FAQ
What are the most common AI development services?
Businesses usually refer to AI in order to automate routine tasks, label data sets for training and evaluation of machine learning models, and to get data-driven predictions from advanced analytics. Also, they implement pre-trained neural networks into their ML solutions to improve efficiency at a lower cost.
What are the business outcomes of AI software solution development?
No matter what industry you are in, your business will surely benefit from our Artificial Intelligence development services. The outcomes may vary across industries. For retail, tapping into new market segments and better targeting of customers’ needs may boost sales and reduce customer churn. The automotive sector will benefit from autonomous vehicles, healthcare will win from automated medical diagnosing, while financial services will get superior fraud detection and security.
Which tools and frameworks do you use for AI development?
Languages: Python, R, Scala, SQL, C++, etc.
Visualization: Power BI, Tableau, Qlik, Matplotlib, seaborn, ggplot2, plotly, Bokeh
DBMS: Relational (MS SQL, PostgreSQL, MySQL), Non-relational (MongoDB, CouchDB, Cassandra etc.), Distributed (Hadoop etc.)
DL Frameworks: PyTorch, Tensorflow, Keras
Architectures: On-premise, cloud, hybrid
Algorithms: Supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction, anomaly detection, pattern search), ensembles, reinforcement learning
Fields: Natural Language Processing, Computer Vision, Recommendation systems, Tabular data analysis, Signal Processing
Neural Networks: CNN, RNN, DNN, LSTM, GAN, Autoencoders
Cloud Platforms: Amazon Web Services, Google Cloud Platform, Microsoft Azure
