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AI Software Development Services

A pioneer in AI development since 1989, ScienceSoft builds custom AI solutions from simple chatbots and web scraping tools to complex business automation systems. We also help select, develop, train, and maintain machine learning models.

AI Software Development Services - ScienceSoft
AI Software Development Services - ScienceSoft

AI software development is a way to get ML-powered solutions that automate repetitive tasks, instantly process large amounts of data and deliver insights, generate content and visuals, and more.

ScienceSoft’s Head of Data Analytics Department

According to a report by Grand View Research, the worldwide artificial intelligence market is expected to expand rapidly, with AI becoming an integral part of business operations and consumer-facing applications. With an extremely competitive landscape of more than 15,000 AI companies in the US alone, time becomes a deal-breaker in introducing new AI ideas to the market. For companies looking to implement transformative AI-supported solutions, the market offers opportunities leading to revenue increase and poses risks associated with tech and vendor decisions.

Why Choose ScienceSoft for Your AI Initiative

35 years
in AI
professionals on board
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professionals on board

We have all the required talents in-house, including project managers, solution architects, data scientists and engineers, compliance consultants, software developers, UX/UI designers, and QA specialists. Over 50% of our talents are senior-level experts.

AI model accuracy
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AI model accuracy

Our custom AI models have an average accuracy of 95% and achieve an accuracy of up to 98%.

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We have first-hand experience implementing AI for demanding industries such as healthcare, BFSI, automotive, manufacturing, retail & ecommerce, and more.

Trusted by global market leaders

CEO, ScienceSoft

Back in 1989, ScienceSoft was one of the first companies to develop AI and integrate it into a product used by 40% of the Fortune 500. Since then, we have embraced advances in the field but kept a cool head to protect our customers’ interests. We don’t experiment with AI — with three decades of experience, we develop sustainable and secure solutions that deliver measurable results and don’t expose our clients to unnecessary risk.

Explore Our Portfolio of Artificial Intelligence Software

What Our Customers Say About Working with ScienceSoft

Star Star Star Star Star

Right from the start, your team showed professionalism and expertise that immediately put us at ease.

We appreciate that you examined our needs with great diligence and went above and beyond to develop a solution that fully meets our high demands for image quality. We're sure the new app will help us boost our productivity and help us cater to more clients. Special credit to the data scientist: the performance of the new image stitching algorithm is amazing.

Our collaboration was straightforward and efficient.

We have cooperated with ScienceSoft on the evolution of our main product, an Al-powered tool that helps detect and fix vulnerabilities in software code. ScienceSoft's team did a solid job for us. They are extremely competent and committed.

It’s clear they’re fully invested in our project and collaboration.

ServicePulse, LLC commissioned ScienceSoft to help us develop an AI-based software product for a limited number of clients to calculate software development efficiency and customer satisfaction score. They delivered software on time and with the required quality. The team adhered to consistent two-way communication and regularly shared detailed project updates and recommendations on how certain functions could be implemented in a more efficient way.

AI Software Development Services by ScienceSoft

AI software consulting

We can conceptualize your AI software, help select fitting ML models, and propose a scalable, high-performing solution architecture. Our experts advise on tech stack selection, development planning, TCO reduction, model training, regulatory compliance, and much more.

Go for consulting

End-to-end AI-powered software development

Our experts can build AI-powered software of any complexity, from simple tools running on open-source AI models to innovative systems powered by proprietary ML engines. To verify solution feasibility and avoid unnecessary risks, we can start with a proof of concept or an MVP.

Go for AI software development

Adding AI to existing software

We will analyze your current software and IT infrastructure and suggest a cost-efficient and secure way to introduce AI. We can provide advice on ML model choice, training, testing, and integration or handle the entire process of evolving your software with AI functionality.

Go for software evolution

Designing and training AI/ML models

Our data scientists can design and train proprietary AI models, including deep learning networks (CNN, RNN, GAN), for diverse tasks from content generation to natural language processing and image recognition. Our ML models steadily achieve a >95% accuracy.

Go for AI/ML model development

AI Solutions and Capabilities We Build

An AI software development company with hands-on experience in 30+ industries, we tailor AI solutions to the unique needs of each domain, including healthcare, BFSI, manufacturing, retail & ecommerce, advertising, professional services, and more.

General overview

Generative AI (like ChatGPT, DALL-E, MuseNet)

Conversational AI (chatbots, virtual assistants)

Business automation solutions

Image analysis and computer vision

Facial recognition

Text-to-speech and speech-to-text

Recommendation engines and prescriptive AI

Predictive analytics

Fraud detection in digital and physical environments

Autonomous vehicles and ADAS

AIOps solutions

AIoT solutions

Overview by business area

Customer service

  • Virtual customer support agents and chatbots providing field-specific assistance (e.g., in doctor appointment scheduling, insurance claim filing, loan application submission).
  • Converting speech to text and text to speech.
  • AI-based recommendations on optimal actions for human agents.
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Industry-centric AI assistants

Specialized assistants based on generative AI models like GPT-4 can excel in various sectors, including:

  • Healthcare (virtual physical therapy guides, AI scribes).
  • Education (personalized study planners, virtual tutors).
  • Digital advertising and marketing (ad content generators, social media managers).
  • BFSI (virtual mortgage advisors, AI traders).
  • Gaming (life-like NPCs, adaptive virtual opponents).
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Diagnosing, treatment, and medical imaging

  • AI assistance for EHR management (e.g., speech recognition, appointment summaries, smart data entry suggestions).
  • Medical image analysis for MRI, CT, PET, SPECT, X-ray scans, ultrasound images.
  • AI-powered diagnostic assistance and treatment recommendations.
  • Identification of hidden factors that influence health outcomes (e.g., past diagnoses, medication side effects, lifestyle, demographics).
  • 3D body mapping.
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Financial management

  • Financial modeling and cash flow forecasting.
  • Financial fraud detection and prevention.
  • Financial risk management.
  • Expense management to identify cost reduction opportunities and optimize spending.
  • Tax optimization to minimize tax liabilities.
  • Financial reporting and compliance monitoring.
  • Asset and investment portfolio optimization.
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Supply chain management

  • Real-time delivery route optimization and fleet monitoring.
  • AI-assisted supplier selection and supplier performance assessment.
  • Predictive maintenance of warehouse equipment, trucks, and other assets.
  • Computer vision for automated product inspection.
  • Warehouse operations automation with robots and drones.
  • Supplier communication automation (e.g., payment reminders, invoice sharing).
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Inventory management

  • Computer vision for inventory counting.
  • Inventory demand forecasting based on data from all supply chain touchpoints, including customers, suppliers, manufacturers, and distributors.
  • Real-time inventory optimization tools that dynamically adjust safety stock levels, reorder points, etc.
  • Dynamic price optimization to reduce inventory levels by applying discounts.
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Asset maintenance

  • Intelligent insights and recommendations for OEE and asset utilization optimization.
  • Predictive asset maintenance.
  • Asset lifecycle management (e.g., asset replacement or upgrade decisions, depreciation rates monitoring).
  • Identification of asset risks related to regulatory compliance, environment, safety, etc.
  • Real-time energy consumption optimization.

Sales and marketing

  • Multi-dimensional customer segmentation and sentiment analysis.
  • Pipeline management with predictive lead scoring, deal prioritization, and smart recommendations on optimal actions.
  • Dynamic content personalization, including purchase and cross-selling recommendations.
  • Automated email campaigns with personalized content and delivery schedules.
  • Dynamic price optimization based on the demand, stock levels, and the analysis of competitor prices.

HR management

  • AI-powered CV screening and candidate matching.
  • Chatbots replying to common employee inquiries (e.g., on benefits, leave requests).
  • AI-assisted identification of factors contributing to employee turnover.
  • Sentiment analysis to understand employee engagement.
  • Detection of unconscious recruitment bias.
  • Personalized recommendations on employee performance optimization and learning opportunities.
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  • Automated detection of digital fraudulent activity (e.g., money-laundering transactions, phishing attempts, bots, malware, breach attempts).
  • Detection of fraudulent and potentially hazardous activity in physical environments. Instant initiation of relevant security measures in case of threat detection. Voice- and biometric-based authentication in security systems.
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Web scraping and crawling

  • Extraction of case-specific data from websites, social media platforms, customer review portals, news resources, etc.
  • Topic-specific content aggregation.
  • Sentiment analysis.
  • Extracting additional information from logos, product images, and in-picture text with the help of image analysis.
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Content creation

  • Natural language generation (NLG) with capabilities for style and tone tuning.
  • Prompt-based or algorithmic generation of text, images, sound, and video.
  • Subtitle creation and image captioning.
  • Content optimization (e.g., tuning the style, adding keywords for SEO).
  • Content summarization.
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ScienceSoft’s Head of Data Analytics Department

Do you need to develop a custom AI/ML model for your software?

In most cases, no. There is a large selection of available open-source or licensed AI models that can perform common tasks such as speech recognition or content generation. Whenever there are high-quality, pre-trained models that are cheaper and faster to implement than custom ML algorithms, we recommend this option first. For example, we built a solution based on five open-source NN models for a client who wanted to implement NLP for help desk software. At the same time, while open-source models have no upfront costs, they may lack support or comprehensive documentation. And licensed AI models, such as those from Microsoft and Amazon, will require ongoing fees and may have usage restrictions.

If the drawbacks of either option are unacceptable or there’s no suitable pre-trained model in the first place, it makes sense to go custom. I’m talking about cases like medical diagnosing, credit risk assessment, or quality control in car manufacturing — areas where precision and security are non-negotiable. Here, a cost-saving approach would be to tailor and re-train an existing model. However, it’s also possible to build one fully from scratch using publicly available or internal data sets.

If you’d like to learn more about the AI development process, check out our guide.

AI Software Development Costs

AI software development costs can range from $30,000 to $4,000,000, depending on solution type and complexity, the need for proprietary ML model development, the specifics of model integration, and more.

Sample cost ranges for AI software development


Development of an individual AI component (e.g., a forecasting or image analysis module).


Development of an AI-powered virtual assistant.


Building an AI-driven automation solution of average complexity (e.g., for inventory optimization).


Implementation of a complex analytics system powered with AI and big data techs.

AI-Based Software Development Cost Estimation

Please answer a few simple questions about your needs to help our experts calculate the cost and timelines for your AI-based software development project quicker.


*What describes your situation best?

*What is the purpose of the AI solution you want to develop?

*What services are you interested in?

*How do you plan to use your AI-powered solution?

*What platforms should your AI software be available on?

*Do you have any preferences for the environment?

*What software do you want to enhance with AI capabilities?

Is your software custom or platform-based?

*In what environment is your software deployed?

*What platforms does your software currently support?

*What services are you interested in?

*What AI capabilities are you interested in?

*What capabilities does your current AI-powered solution enable?

Is your software custom or platform-based?

*In what environment is your AI-powered software deployed?

*What platforms does your AI-powered software currently support?

*Do you want to expand your software to new platforms?

*What aspect(s) of your AI-powered software would you like to improve?

*What services are you interested in?

*Do you have any requirements for the minimum accuracy rate of AI model output?

*Would you require any integrations?


With corporate software, external data sources, third-party services (user authentication, payment systems), etc.

*Do you have any tech stack preferences?


Particular programming languages, software platforms, cloud services, etc.

*Do you have any software compliance requirements? Check all that apply.

Your contact data 

Preferred way of communication:

We will not share your information with third parties or use it in marketing campaigns. Check our Privacy Policy for more details.

Our team is on it!

ScienceSoft's experts will study your case and get back to you with the details within 24 hours.

Our team is on it!

Machine Learning Models and Technologies We Work With

ML models

Neural networks, including deep learning

  • Transformer models, large language models (LLMs).
  • Convolutional and recurrent neural networks (including LSTM and GRU).
  • Autoencoders (VAE, DAE, SAE, etc.).
  • Generative adversarial networks (GANs).
  • Deep Q-Networks (DQNs).
  • Feed-forward neural networks, including Bayesian deep learning.
  • Modular neural networks.

Non-neural-network machine learning

  • Supervised learning algorithms, such as decision trees, linear regression, logistic regression, and support vector machines.
  • Unsupervised learning algorithms: K-means clustering, hierarchical clustering, etc.
  • Reinforcement learning methods, including Q-learning, SARSA, and temporal differences method.


Frequently Asked Questions on AI Software Development

Privacy breaches have been making headlines. Say we use AI to process customer data — how do we build privacy protections into the app?

At ScienceSoft, we start artificial intelligence development by creating a 100% secure environment for data processing and storage during AI development, applying our ISO 27001-certified security management system and DevSecOps best practices throughout the SDLC. If we use sensitive data to train an AI model, we anonymize it to avoid the risk of data breaches.

To make sure the AI solution itself doesn’t pose unnecessary risks, we implement data encryption at rest and in transit and robust role-based access control mechanisms. Additionally, we employ data masking, enforce strict logging and monitoring practices, and utilize advanced threat detection mechanisms such as ML-based intrusion detection.

Our compliance experts make sure the AI solution aligns with regional and industry-specific regulations and standards, including HIPAA, GDPR, KYC/AML, and more. We also guarantee transparency for users: they get clear explanations of what personal data is being collected and what for and are asked for consent to data collection and processing.

We’re planning an AI initiative but doubt its feasibility. How do we know AI will work out for our case?

In such cases, we recommend starting with a proof of concept to check the idea’s feasibility in the shortest possible timeframe. Designing a proof of concept (PoC) is a good way to showcase how the solution will work, estimate the potential value, address major concerns, and draw up a risk mitigation strategy. PoC is also the best choice for a startup company to get a demo version of the future app and use it to attract investments. PoC is highly recommended for innovative AI solutions, where there may be several technology choices that haven't been tested before.

We’re currently shortlisting vendors and planning our AI budget. Do you have a price list for your services?

To provide exact cost estimates for an AI initiative, we first need to complete a project discovery, but we understand that our customers often require a quote much earlier than that. To satisfy these needs, we offer ballpark quotes (use our online calculator to get one) and give preliminary estimates at early project planning stages (e.g., using T-shirt sizing or PERT methods). When it comes to the final quote, we provide a detailed cost breakdown and draw up a contingency budget to make sure our customers know exactly what they are paying for. Feel free to explore our cost estimation practices in the dedicated guide.

We've heard that data quality is one of the most critical factors for AI success. We don't know if our data is of sufficient quality.

Indeed, data quality largely determines AI output accuracy. However, quality is not an inherent or objective attribute of any data set. Each project has different requirements, so even if the quality of your data is lower than expected, our data engineers can improve it to achieve the desired level. Our professionals use automated tools to assess, cleanse, and deduplicate the data to avoid human error and save time. In case your data is insufficient, we can also enrich it by using external sources (e.g., financial data marketplaces, social media, GIS).

How reliable is AI output? Will we need human staff to check and control it?

The need for human involvement depends on the case. High-risk tasks like medical image analysis may require constant human presence to verify the AI output, while lower risk tasks (e.g., data entry) will require zero or close to zero human participation. Here are some of the key factors that affect AI output quality, depending on the use case:

Data quality and quantity. Training data should be clean, relevant to the use case, and representative of the future input that AI will process. Since larger datasets often lead to a higher quality of output, we strive to collect as much data as necessary and can augment the data sets provided by our customers. For example, we can get additional data from relevant online sources with the help of web scraping tools or use generative adversarial networks (GANs) to generate synthetic data for the training set.

Model selection and training. Depending on the project specifics, we select ML models that will ensure adequate output accuracy and an acceptable cost-to-performance ratio. For highly innovative cases, we develop custom ML models.

Model validation and testing. We implement robust ML validation and testing mechanisms, including cross-validation.

Evaluation metrics. We define and apply clear evaluation metrics that align with the AI solution’s goal. Common metrics include precision, recall, F1-score, and mean squared error. We monitor and evaluate model performance using these metrics.

Human-in-the-Loop (HITL). Depending on the use case and criticality of the output, it may be necessary to implement the Human-in-the-Loop (HITL) system. This involves human reviewers who can validate or adjust the AI output when necessary. It may be recommended for cases like content moderation, medical diagnosing, and legal document review.

Feedback loop. After every iteration, the AI output is submitted for an expert review. The feedback is then incorporated into the next version of the model to improve its accuracy.

Monitoring and alerting. We can implement monitoring and alerting systems to detect anomalies or drops in model performance. This allows for proactive intervention when AI accuracy degrades.

AI is known to be prone to biases and may violate human rights. How do we avoid it?

Currently, the best way to avoid harmful biases in AI output is to build your software in alignment with UNESCO's Human Rights Approach to AI. To do this, we recommend starting artificial intelligence software development with Human Rights Impact Assessments (HRIA) to identify potential cases where the technology may affect individuals’ rights. When conducting the research, it’s essential to combine domain expertise with feedback from multiple stakeholders, including potential end users and representatives of affected communities.

Ready to Talk Specifics?

Tell us about your AI initiative — ScienceSoft is here to apply our decades of experience to make AI work for your case without unnecessary risks.