Artificial Intelligence (AI) Software Development

Get end-to-end custom AI solution development services from an award-winning provider and reliable IT innovation partner!

Unicsoft’s ISO certified team is well-equipped for custom AI solutions development compliant with EU AI Act & GDPR regulations.

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AI systems introduce automation at the core of every operation — human cognitive functions.
The global Al market is projected to account for USD 407.0 billion by 2027, growing at a CAG of 36.2%.

Our Artificial Intelligence Expertise Covers All Use Cases

Text analytics

Natural language processing

Speech recognition

Emotion recognition

Image recognition

Natural language generation

Virtual agents

AI-optimized hardware

Decision management and AI-analytics

Bio-metrics

Content automation (including SEO copywriting)

Recommendation systems

Computer vision

Tailored AI Solutions We Have Developed

Empowering Every Industry with Next-Gen Custom AI Solutions

Sentiment Analysis in Consumer Industries

Sentiment Analysis in Consumer Industries

Customer sentiment analysis by means of NLP helps the company classify the negative and the positive impact of its service or product through analyzing the changed customer behavior, preferences, and patterns.
Real-time Customer Recommendations

Real-time Customer Recommendations

Utilizing machine learning and data-in-motion techniques, we build mobile engagement platforms with Edge AI to engage with customers in specific moments, sort customers based on their behavior, target ads, and tweak the user experience on the go with a software compliant with GDPR, CCPA, CPRA, CDPA regulations.
Computer Vision for Customer Behavior Tracking

Computer Vision for Customer Behavior Tracking

In combination with existing security cameras, algorithms can automatically evaluate video material and study customer behavior to optimize the design and placement of products, avoid traffic jams in well-visited departments, track the popularity of products.
Image Recognition for Shelf Monitoring

Image Recognition for Shelf Monitoring

With image recognition solutions, you can easily make store audits and real-time shelf monitoring to get accurate insights on direct sales, marketing ROI, competitors’ price analysis, on-shelf missing products, as well as improve your in-shelf SKU visibility, ensure optimal Share of Shelf and lift product sales in every store.
 Predictive Machine Maintenance

Predictive Machine Maintenance

Using AI technologies for monitoring machines and collecting feedback data in real-time, you can analyze patterns for each machine to determine its actual maintenance needs and create a customized schedule that minimizes overall downtime on the factory floor.
 Fintech Fraud Detection and Analytics

Fintech Fraud Detection and Analytics

Financial organizations deploy machine learning models that can detect suspicious transactions in real-time, analyze events that could indicate an account is being used to conceal funds, predict potential fraud in future transactions, and then use anomaly detection to spot unusual account activities.
Risk Assessment in Insurance

Risk Assessment in Insurance

Machine learning models help accurately assess risk with less information. This creates an opportunity to simplify insurance applications and remove invasive tests and questions, identify different categories of risk, and make the entire process much more user-friendly.
Investment Predictive Analytics

Investment Predictive Analytics

Machine learning-enabled technologies assist banking and financial institutions in identifying the best investment plan and give advanced market insights allowing the fund (portfolio) managers to identify hidden market opportunities much earlier as compared to the traditional investment analytics models.
Computer Assisted Diagnosis

Computer Assisted Diagnosis

AI can analyze vast amounts of medical data from a wide range of sources, uncovering complex patterns and disease characteristics that humans might miss out on. Through the application of deep neural networks, machine learning, and categorization, medical practitioners make a more accurate and efficient analysis of patient data.
 Smart Prediction and Digital Supply Network

Smart Prediction and Digital Supply Network

Use AI to accelerate demand planning, improve demand signals, and tightly integrate cross-functional supply chain processes. Predictive analytics uses statistical algorithms to forecast future trends, which enables businesses to optimize inventory, improve delivery times, increase sales and reduce operational costs.
Fraud Detection in Gaming

Fraud Detection in Gaming

Machine learning algorithms help make use of the data game developers already have: detect and prevent fraudulent activity while optimizing a frictionless customer experience, prevent costly fraud losses, automate escalation and take action against cases of suspicious behavior.

If you are looking for a custom AI development company, contact us for a free consultation.

Request a consultation
In addition to project planning and development, Unicsoft helps clients understand which AI solution is best for their case. Thoroughly planning a project's architecture can future-proof your company by significantly cutting costs on scaling and integrations. Request a consultation

Robust AI-powered business solutions development

In most cases, AI software developers present tech advancement as an exclusive and expensive service. In many ways, this is true, given that two years ago there were only 22 thousand specialists in this field in the world. Yet, being a reliable AI development services provider, we tend to reduce the price of AI development by preventing possible setbacks in product implementation.

AI model development
process we stick to

01
Define business requirements
We work closely with our client to understand the project's objectives and transform them into a problem definition with specific and clear milestones and a suitable plan for solving it. Starting the project from a discovery phase helps us to define its “success” criteria and measurable ROI.
02
Analyze data
We identify available data sources that can be used for training AI models and determine whether they have proper metrics: quantity and quality of training data, how to split test data from the training dataset, ways to label data, etc. If there is no open data available, we consult customers on how they can collect them.
03
Data collection
Our data engineer is responsible for collecting data from various sources, standardizing its formats and consolidating in a centralized storage (Data Lake or Data Warehouse), and building for this purpose ETL (Extract, Transform, and Load) pipeline.
04
Data preparation
Our data scientist does a number of activities to prepare data to be used for modeling: data cleaning, data labeling, enhancing and augmenting data, removing irrelevant data, reducing noise and ambiguity, anonymizing data, sampling data from large data sets.
05
Feature modelling
We select features that are the most important for prediction, decide on the most relevant algorithm for model training, set and adjust model hyperparameters, identify features with best results, develop ensemble models, and test model versions for performance.
06
Model’s performance evaluation
We evaluate which model version has proved to have the highest accuracy and potential and determine whether it meets the business goals of our client. We evaluate different model metrics (confusion, KPIs, model performance, model quality) to understand how it will work in real life.
07
Project go-live estimation
Based on the data we have received during the R&D phase, our team makes a thorough estimation of future AI product development roadmap: resources and data needed for model implementation and further functioning, team composition, model deployment, and data warehouse setup, UI/UX & web development, testing, CI/CD, etc.
08
Working model optimization
Once we have put our AI model into operation, we measure & monitor its performance (model accuracy results), create benchmarks for future iterations, continuously iterate on different aspects and add new parameters to increase the model’s overall accuracy, work on data biases, and noise.
01. Define business requirements
We work closely with our client to understand the project's objectives and transform them into a problem definition with specific and clear milestones and a suitable plan for solving it. Starting the project from a discovery phase helps us to define its “success” criteria and measurable ROI.
02. Analyze data
We identify available data sources that can be used for training AI models and determine whether they have proper metrics: quantity and quality of training data, how to split test data from the training dataset, ways to label data, etc. If there is no open data available, we consult customers on how they can collect them.
03. Data collection
Our data engineer is responsible for collecting data from various sources, standardizing its formats and consolidating in a centralized storage (Data Lake or Data Warehouse), and building for this purpose ETL (Extract, Transform, and Load) pipeline.
04. Data preparation
Our data scientist does a number of activities to prepare data to be used for modeling: data cleaning, data labeling, enhancing and augmenting data, removing irrelevant data, reducing noise and ambiguity, anonymizing data, sampling data from large data sets.
05. Feature modelling
We select features that are the most important for prediction, decide on the most relevant algorithm for model training, set and adjust model hyperparameters, identify features with best results, develop ensemble models, and test model versions for performance.
06. Model’s performance evaluation
We evaluate which model version has proved to have the highest accuracy and potential and determine whether it meets the business goals of our client. We evaluate different model metrics (confusion, KPIs, model performance, model quality) to understand how it will work in real life.
07. Project go-live estimation
Based on the data we have received during the R&D phase, our team makes a thorough estimation of future AI product development roadmap: resources and data needed for model implementation and further functioning, team composition, model deployment, and data warehouse setup, UI/UX & web development, testing, CI/CD, etc.
08. Working model optimization
Once we have put our AI model into operation, we measure & monitor its performance (model accuracy results), create benchmarks for future iterations, continuously iterate on different aspects and add new parameters to increase the model’s overall accuracy, work on data biases, and noise.

What our clients say

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What our clients say

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Get in touch for a consultation. It's free and we'll help you identify the best AI solution for your needs.

Potential challenges you need to be aware of

Stakeholders’ lack of AI understanding

This issue has nothing to do with development. However, it often inhibits the best results. The lack of people who, at least in general, understand the scope of the technology and the implementation process leads to frustration and the inability to correctly implement a completed solution into the business process.

Data mining and data storage

The quality of work for AI solutions directly depends on the quality and volume of data for training ML systems. In some specific projects, as well as entire industries like industrial applications, it is simply impossible to collect enough data. Also, you should not only have data but also be able to store massive amounts of information for its analysis. This creates an additional load on the existing infrastructure.

Data organization and cleanliness

It’s not enough to have access to big data. The data should also be properly organized, and the datasets should not include data impurities that are not related to the ML model training subject. The work of a data analyst is imperative before developing a custom AI solution.

Lack of computational speed

The speed of system training based on machine learning is proportional to the computing power at the disposal of these systems. Your AI developer can create an ML model and test it, but they cannot provide the processing power. This means you need to consider an investment in infrastructure or prepare to achieve results over a sometimes very long period of time.

Lack of specialists and market demand

AI development services today are the new black. Thousands of enterprise customers align themselves with the few who can do a job well. This means that, unlike a normal development cycle, you need to be aware of the fact that it will not be possible to start implementing the project quickly.

Data privacy and security

Considering that most ML models operate on a database describing user behavior and identity, this data has to be protected from leaks. The GDPR and other regulations expressly prohibit the transfer of this data to third parties. This means that any work of ML models is possible only in an environment controlled by the customer (on corporate servers, without transferring any data to developers).

Why Unicsoft?

Cross-domain expertise
Unicsoft has successfully positioned itself ahead of artificial intelligence trends for over 10 years. Our battle-hardened developers know how to take emerging technologies and turn them into revenue-generating tools for businesses.
Delivered dozens of AI/ ML projects
We’ve built custom AI-powered software solutions for various industries, including fintech, retail, education, supply, and others.
Award-winning company
Unicsoft is a world-renowned AI development company featured in the top vendor lists of Clutch, GoodFirms, and Hackermoon.
Full-cycle software development
At Unicsoft, we have a reliable pool of AI/ML developers, data scientists, and other tech talents as well as fast and well-structured software development processes.
Unicsoft is a one-stop destination for organizations striving to launch AI-automated business solutions, analytics, and augmented products.

3 domains that drive AI adoption across multiple industry use cases

Cognitive engagement
Cognitive engagement
Transforms how humans interact with computer systems. Sequential data input/output operations are replaced with human-like conversations. From a graphical communication interface, the interaction can then be transformed into a vocal or even visual experience.
Cognitive understanding
Cognitive understanding
Artificial intelligence systems process, structure, and select the data that is most relevant to an individual. AI reduces the stress associated with the modern world's information overload.
Cognitive automation
Cognitive automation
AI can recognize logic in human actions and mimic it. Thus, AI can automate the vast majority of repetitive workflows without hardcoding each of them.

The technology Unicsoft uses to create AI systems

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DL Frameworks

PyTorch
Tensorflow
Keras
MXNet
Nvidia
Caffe
Caffe2
Chainer
Theano
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Neural Networks (NN)

GAN
Long Short-Term Memory NN
Deep NN
Autoencoders
Representation learning
Manifold learning
Bayesian NN
Autoregressive NN
Convolutional NN
Recurrent NN
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Algorithms

Supervised / unsupervised learning
Clustering (all types)
Metric learning
Few-shot learning
Ensembles
Reinforcement learning
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Supported Databases

MS SQL
PostgreSQL
MySQL
MongoDB
Cassandra (and other non-relational DBs)
Hadoop (and other distributed DBs)
CouchDB
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Supported Cloud Providers

AWS
Google Cloud
Microsoft Azure
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Toolkits

Core ML
Microsoft Cognitive Toolkit
Kurento’s computer vision module

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F.A.Q

What are the business benefits of AI development services?plus
The main challenges in AI software development are the following:
- Data quality and volume
- Proper data organization (labeling, cleanup, etc.)
-Computational power of the project infrastructure
- Data privacy and security
Each challenge’s level of difficulty varies from project to project and can be planned and estimated in advance by a team of professionals.
How long does it take to scope, plan, and implement an AI project?plus
AI projects scale varies significantly depending on what kind of project you want to implement (just a proof of concept or a prototype, MVP or a fully functional model/software integrated into other systems), and level of its complexity (please refer to our “challenges” section). In general, you should aim for 1 month for a prototype/PoC, and for 3 to 6 months for an MVP. These are almost always the necessary steps during the AI software development process, after which you get a precise estimate for the following phases.
What artificial intelligence development services does Unicsoft provide?plus
We offer the full-cycle AI software development services that include working with datasets, prototype/PoC/MVP development, sophisticated AI software development, existing AI software optimization, refactoring, evolution, integration, and support. We are experts is natural language processing, image recognition and processing, predictive analytics, scoring models, automations, and bots.
How to choose a healthcare software development company?plus
When selecting a healthcare software development company, prioritize their proficiency in navigating healthcare regulations such as HIPAA and GDPR. Evaluate their track record and portfolio, and ascertain client testimonials to gauge reliability and quality. Ensure they provide robust support and maintenance, and confirm they comprehend your unique requirements and offer a transparent development process.
How can AI software development services help improve the efficiency of my business?plus
AI software developed by Unicsoft can enhance your business's efficiency by automating repetitive tasks, optimizing workflows, and providing data-driven insights. This helps reduce errors, streamline operations, and improve decision-making, allowing your team to focus on higher-value activities and cut operational costs.
What specific AI solutions can you offer for my industry?plus
We offer the strongest AI solutions tailored to suit a variety of industries. Our advanced sentiment analysis, Edge AI, and computer vision technologies provide value for consumer services. ML models are highly effective in fraud detection, risk assessment, and enhancing customer experiences in fintech, gaming, and insurance. In healthcare, we develop AI solutions for accurate diagnoses and routine tasks automation. Additionally, our predictive analytics solutions for supply chain management optimize inventory, delivery times, and operational costs, driving both innovation and efficiency.
What are the steps involved in the AI software development process?plus
Our process begins with a thorough understanding of your requirements, followed by the collection and preparation of data for AI models. Once the most suitable algorithms have been selected and trained, they are tested to ensure optimal performance. We then integrate the solution into your systems, deploy it, and provide ongoing monitoring and updates.
Have an idea for an AI project?
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