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 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.
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.
Trusted by global market leaders
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 clients’ 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 makes ScienceSoft different
We achieve project success no matter what
ScienceSoft does not pass off mere project administration for project management, which, unfortunately, often happens on the market. We practice real project management, achieving project success for our clients no matter what.
AI Software Development Services by ScienceSoft
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
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
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
$30,000–$200,000
Development of an individual AI component (e.g., a forecasting or image analysis module).
$120,000–$300,000
Development of an AI-powered virtual assistant.
$200,000–$600,000
Building an AI-driven automation solution of average complexity (e.g., for inventory optimization).
$800,000–$1,000,000+
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.
Thank you for your request!
We will analyze your case and get back to you within a business day to share a ballpark estimate.
In the meantime, would you like to learn more about ScienceSoft?
- Project success no matter what: learn how we make good on our mission.
- 4,000 successful projects: explore our portfolio.
- 1,300+ incredible clients: read what they say.
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.
Technologies
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 clients 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 clients 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 clients. 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.