How to Develop AI Software

Project Roadmap, Team, Sourcing Models

Having 34 years of experience in data science and AI, ScienceSoft knows how to create powerful software with artificial intelligence (AI) capabilities promptly and with minimal risks.

How to Build Software Powered By Artificial Intelligence - ScienceSoft
How to Build Software Powered By Artificial Intelligence - ScienceSoft

The Essence of Developing Software with AI Capabilities

Development of software with AI capabilities implies building new software or evolving existing software to output AI analytics results to users (e.g., demand prediction) and/or trigger specific actions based on them (e.g., blocking fraudulent transactions). 

Supported by AI, an application can automate business processes, personalize service delivery and drive business-specific insights. According to Deloitte, 94% of business leaders agree that “AI is critical to success over the next five years”.

ScienceSoft helps both enterprises and product companies plan and build full scale AI solutions for 30+ different industries, including manufacturing, healthcare, energy, retail and wholesale, professional services,financial services, and telecommunications.

Use cases for software with AI capabilities

Business process automation

  • Chatbots
  • Search engines
  • Automated document generation
  • Optical character recognition engine for data extraction from paper documents
  • Job candidates screening and shortlisting

Production management

  • Predictive maintenance
  • Demand and throughput forecasting
  • Process quality prediction
  • Production loss root cause analysis

Customer analytics

  • Sentiment analysis
  • Customer behavior prediction
  • Sales forecasting

Risk management

  • Counterparty risk analytics
  • Potential damage prediction
  • Fraud detection

Supply chain management

Personalized service delivery

  • Customer segmentation
  • Recommendation engines

How to Develop Artificial Intelligence Software in 8 Steps

The duration and sequence of the development stages will depend on the scale and the specifics of both basic software functionality and artificial intelligence you want to enrich it with. Below we present a generalized process outline based on ScienceSoft’s 33-year experience in software development and data science.


Feasibility study

Pavel Ilyusenko

A best practice from ScienceSoft’s PMs: Pavel Ilyusenko, Head of PMO, says:

“To save on time and budget resources and increase the ROI of AI, we deliver a PoC to uncover possible AI-related roadblocks, such as low-quality data, data silos, data scarcity.”



Business analysis to elicit AI requirements


Solution architecture design


Business processes preparation (in case of software development for internal use)


Software development (non-AI part)


AI module development


AI deployment


Maintenance and evolution of AI-powered software

Consider Professional Services for Development of AI-Powered Software

ScienceSoft applies 34-year experience in software development and data science to create solid software with AI capabilities.

Consulting: software development with AI capabilities

  • A feasibility study on integrating AI into your software (potential benefits, risks, and costs).
  • A risk management strategy to mitigate AI-related risks.
  • A development, deployment and integration plan.
  • Choosing an optimal sourcing model.
  • An efficient technology stack for software and its AI part.
Discuss consulting services

Outsourced development of software with AI capabilities

  • Feasibility study (including PoC).
  • Eliciting requirements for software and AI.
  • Software development and testing.
  • AI development: data preparation, ML model building, training and tuning.
  • AI integration and testing.
  • User training.
  • Software maintenance and evolution.
Outsource development
  • In software development since 1989.
  • In data science and data analytics since 1989.
  • In business intelligence and data warehousing since 2005.
  • In big data services since 2013.
  • In image analysis consulting and development services since 2013.
  • Average experience of our PMs, BAs, solution architects, developers, data analysts, and other IT professionals: 7-20 years.
  • Quality-first approach based on a mature ISO 9001-certified quality management system.
  • ISO 27001-certified security management based on comprehensive policies and processes, advanced security technology, and skilled professionals.
  • For the second straight year, ScienceSoft USA Corporation is listed among The Americas’ Fastest-Growing Companies by the Financial Times.

Software Powered by AI: ScienceSoft’s Success Stories

AI Strategy Consulting for Electric Energy Consumption Forecasting

Facial Recognition Software for Retail

  • Preprocessing with specifically processed input images to make customers' faces recognizable.
  • Calculating landmarks, i.e. some particular facial features.
  • Face recognition using calculated landmarks and special criteria.
AR Advertising Software Optimization

Software for Remote Monitoring of LED Displays

  • Detecting and classifying problems with displaying images
  • Reporting the results to the web server through HTTP requests in real time.
Defect Recognition Software for Oil Drilling Equipment

Defect Recognition Software for Oil Drilling Equipment

  • Monitoring and analysis of the drilling equipment.
  • Timely detection of drill bits’ defects.
  • Recommendations on required drill bit replacement and maintenance.
Software for Defect Recognition in Polyurethane Film

Software for Defect Recognition in Polyurethane Film

  • Detecting and reporting on film defects in real time.
  • Running root cause analysis of defects and providing tailored recommendations for product quality increase.
Brain Tumor Localization Application

Brain Tumor Localization Application

ScienceSoft developed a CNN-based application to:

  • Analyze brain MRI scans.
  • Define each brain tissue type.
Software for Remote Monitoring of Oil Storage Tanks

Software for Remote Monitoring of Oil Storage Tanks

  • Remote detection of the liquid level in oil tanks.
  • Timely detection of oil leaks.

ScienceSoft as a reliable AI consulting and development partner

Two years ago, we commissioned ScienceSoft to audit and upgrade our partially developed AI-based software for clay pigeon shooting tracking. 

ScienceSoft ramped up a development team consisting of two C++ developers, two data scientists, and a UI design expert to fulfill the project. The team identified core errors, which didn’t allow efficient solution operation, and implemented high-speed convolutional neural networks to fix them. As a result, the system could track a flying target in a real-life outdoor environment and faultlessly detect shooter’s performance.

Simen Løkka, CEO, Travision AS

Typical Roles in Our AI Solution Development Teams

The roles required in a software development project with an AI part vary according to the project’s goals and scope. The key roles include:

Project manager

To outline a project roadmap, manage the software & AI development life cycle, and foster collaboration between business and tech stakeholders.

Business analysts

To analyze business and user needs and translate them into technical requirements for software, AI, and integration between them.

Data scientists

To cleanse data for AI and engineer features; to build, train, test, and validate ML models. Domain experience is preferred.

Data engineer

To deploy AI and monitor it in production.

UX and UI designers

To design wireframes, create user stories and UI prototypes for AI-driven software, following the principles of user-centricity.

Software developers

To build the software back end and front end and build and implement APIs necessary for integration with AI, and further evolve software.

QA specialists

To design and implement a test strategy to validate software quality.

Sourcing Models of Developing Software with AI Capabilities

All resources are in-house

Full control over the project, however, the lack of the required skills in AI is likely. Growing in-house AI capabilities can be a strategic decision if the development of software with AI functionality is a part of company-wide adoption of AI technologies.

All resources are in-house, except for data scientists

High control over the project and access to competencies unavailable in-house. If you’re looking to grow an end-to-end in-house team in the future, look for a resource vendor who provides knowledge sharing.

Non-AI part is developed in-house, while the AI part is outsourced

Optimal resource usage and access to competencies unavailable in-house. However, establishing smooth team collaboration may pose a challenge.

PM and BA are in-house, all technical resources are external

Sufficient control over the project and better process transparency, no problems with resource utilization after the project. There should be properly qualified PM and BA in-house.

Complete outsourcing

Access to rare talent and the latest technologies, which results in faster development and lower costs but higher vendor risks. Thus, we suggest requesting PoC from a chosen vendor.

Benefits of AI-Powered Software Development with ScienceSoft

Data protection. Always obtaining informed consent for personal data collection and processing.

Strong data security. Creating a protected environment (including DevSecOps practices and tools) for data processing and storage.

Compliance. AI-powered solutions are fully compliant with industry and legal requirements (HIPAA, GLBA, GDPR, etc.).

Data quality. Certified data engineers and data scientists, a wide set of tools to automate data validation, cleansing, reduplication processes.

Guaranteed value of the AI solution. Starting with a PoC, increasing the accuracy of the output by using a combination of white box and black box AI models.

Tracked quality of analytics insights. Output quality KPIs: insights by value (high / average / low); forecast accuracy; missing alerts; business result-related KPIs, etc.

Get Expert Help to Build Software with AI Capabilities

Having 33-year experience in software development and data science, ScienceSoft delivers reliable software with AI capabilities.

Cloud Services ScienceSoft Uses to Speed Up Development of AI Solutions

AI platforms help quickly set up, automate and manage each stage of the AI module development with pre-configured infrastructure and workflows. ScienceSoft recommends considering platforms by major cloud providers: Amazon, Microsoft, and Google. All of them are leaders in Gartner’s Magic Quadrant for Cloud AI Developer Services and offer integrated development environments (IDEs) with the following capabilities:

  • Custom modeling with R/Python and supported frameworks (TensorFlow, PyTorch, scikit-learn, and others).
  • AI workflow orchestration and management.
  • Bias detection, explainability features, etc.
  • Automated model tuning.
  • Model performance monitoring.
  • Autoscaling of compute resources.
  • Advanced security.

Some of the platforms’ distinctive features are outlined below:

Amazon SageMaker


  • Powerful, enterprise-ready infrastructure offered by AWS (e.g., Amazon EC2 and Amazon S3-based) to support AI-related projects.
  • Pre-configured data labeling workflows, access to pre-screened vendors offering data labeling services.
  • One-click data import, 300 pre-configured data transformations, data visualization capabilities.
  • A unified repository to store, organize and reuse ML features.
  • Marketplace with pre-built ML algorithms and models.

best for

Enterprise-scale AI integration initiatives.


Payment for compute and storage resources consumed. Pricing depends on the region, the services used within the platform, their configuration and hours of usage.

Azure Machine Learning Services


  • Drag-and-drop UI for low-code model development.
  • Data labeling service to manage and monitor labeling projects and automate iterative tasks.
  • Flexible deployment options offered by Azure, including the hybrid cloud.
  • Cost management with workspace and resource level quota limits.

best for

Flexible AI deployment (on-premises/hybrid cloud).


Payment for compute and storage resources consumed. Pricing depends on the region, the services used within the platform, their configuration and hours of usage.

Google AI Platform


  • Accelerated AI performance due to integrated proprietary Tensor Processing Unit (TPU).
  • Advanced support of Kubernetes orchestration.
  • Integration with BigQuery (Google’s hyperscale data warehouse) datasets.
  • Data labeling service that connects companies with human labelers.
  • Support of TensorFlow Enterprise.
  • Pre-configured virtual machines and optimized containers for AI based on deep learning.

best for

Integration of resource-intensive deep learning AI into software; startup-friendly.


Pricing depends on the region, the services used within the platform, their configuration (type and number of instances) and hours of usage.

Cost Factors of Developing Software with AI Capabilities

The cost of software powered by artificial intelligence can vary greatly. The estimates rely heavily on the specifics of AI module development:

  • Data volume used for AI and the number of data sources to process.
  • Data type (unstructured data is more expensive to work with than structured).
  • Data origin (there may be a need to buy external data) and whether it needs labeling (tagging data samples with the desired output).
  • Data quality (issues in data require more resources for cleansing).
  • Required accuracy rate for AI (the higher it is, the more time-consuming and expertise-demanding ML model tuning will be).
  • Complexity of ML algorithms.
  • Deployment type (AI outputs are in batches or in near-real-time).
  • AI maintenance costs (AI operating in a changeable data environment, e.g., feeding on dynamic user data, needs regular retraining).
  • Infrastructure costs.

Sample costs


For an AI-powered solution that automatically extracts unstructured data from several sources, classifies it using an ML algorithm of modest complexity, and provides outputs in batches.


For a more complex AI-powered solution that processes data of various types and from a large number of sources with an advanced, expertise-demanding ML algorithm of high accuracy (as it’s critical to business processes)

About ScienceSoft

About ScienceSoft

ScienceSoft is an international IT consulting and software development company headquartered in McKinney, TX. Relying on 34-year practice in software development and data science for 30 industries, including manufacturing, healthcare, financial services and retail, we develop software enhanced with AI to optimize workflows and reduce operating costs, improve decision-making, and increase customer engagement.