How to Develop AI Software
Project Roadmap, Team, Sourcing Models
In data science and AI since 1989, ScienceSoft helps companies from 30+ industries create powerful software with artificial intelligence (AI) capabilities – promptly, cost-effectively, and with minimal risks.
Developing Software with AI Capabilities: Summary
Development of software with AI capabilities is a way for enterprises, tech startups, and software product companies to obtain an innovative solution that enables the end-to-end automation of tasks traditionally involving human intelligence.
Supported by AI, an application can automate even the most complex business operations, personalize service delivery, and deliver valuable case-specific insights. According to Deloitte, 94% of business leaders agree that AI is critical to success over the next five years.
How to build AI software in 10 steps
- Analyze the business needs and conduct a feasibility study
- Define the core features
- Design the solution architecture, UX, and UI
- Code the server and client sides of AI software
- Run necessary testing procedures
- Consolidate, analyze, and clear needed data
- Build and train AI model
- Deploy and integrate the AI software
- Introduce an AI adoption strategy
- Ensure ongoing evolution and support
With decades-long experience in AI and 750+ IT professionals on board, ScienceSoft can provide all the necessary competencies to help companies design and build top-quality AI software. We create flexible AI solutions with capabilities tailored to each customer’s unique requirements and guarantee AI compliance with the relevant legal frameworks.
Examples of AI-Powered Solutions ScienceSoft Develops
- Intelligent root cause analysis.
- Trend and anomaly identification.
- Forecasting quantitative values and events.
- Analytics-based object clustering and segmentation.
- Data-driven prescription of optimal operational decisions.
- Automated enforcement of pre-defined actions (for apps, smart robots, autonomous vehicles, etc.).
- AI-supported search and navigation.
- Image recognition and classification, including facial and emotion recognition.
- Dynamic and static object detection within an image or video stream.
- Action taking when certain visual patterns are detected.
Natural language processing
- Optical character recognition and sentiment analysis.
- Automated language translation.
- Speech recognition and transcription.
- Enforcing textual and voice commands.
- Processing written and spoken human requests.
- Real-time generation of relevant responses.
- Human-like textual and voice communication.
- User prompt processing and real-time conversation.
- Producing new forms of creative content (text, images, audio, video, and more) based on specified parameters.
Industry-Specific AI Benefits We Help Unlock
- Streamlined and more accurate identification of diseases, side effects, and factors that affect health conditions due to AI-supported diagnostics and medical image analysis.
- Improved quality and safety of patient treatment thanks to intelligent prescriptions and instant risk factor identification (e.g., drug interactions, potential side effects).
- Early detection of potential health issues with AI-enabled remote patient monitoring.
- Streamlined population health analytics across large-scale, heterogeneous data sets.
- Faster onboarding of individual and corporate clients thanks to AI-supported validation of client data.
- Minimized risk of illegitimate access to banking services due to intelligent verification of AML/CFT and OFAC compliance.
- Enhanced protection of customer funds and personal information with AI-powered detection of transactional and identity fraud.
- Lower cost of interbank settlements and foreign exchange transactions thanks to AI-based analysis of real-time FX rates and prescriptions on the optimal transaction time.
- Increased loan origination volume thanks to accurate AI-driven borrower risk assessment and automated decisioning on loan approval or rejection.
- Increased productivity of the loan servicing teams and reduced DSO and bad debt due to AI-supported debt collection.
- Higher investment profitability and minimized risk of financial losses thanks to intelligent guidance on optimal investment and hedging tactics.
- Eliminated manual routine and zero lost yield opportunities thanks to automated trading execution.
- Improved business profitability thanks to AI-based analysis of customer risks and suggestions on the optimal personalized policy prices.
- Streamlined damage evaluation with AI-based remote damage inspection and data-driven loss estimation.
- Instant detection of fraudulent insurance claims due to AI-powered verification of claim-relevant data and alerts on faked information.
- Access to innovative insurance models like AI-enabled PAYL insurance and parametric insurance.
Retail and ecommerce
- Minimized risk of stockouts and overstocking due to precise AI-based demand forecasting and multi-echelon inventory optimization.
- Higher revenue due to AI-based price optimization and dynamic pricing.
- Optimized shelf space and sales growth thanks to AI-based planogram design for more ergonomic and engaging product placement.
- Instant theft detection thanks to computer-vision-based monitoring of selling and storage facilities.
- Increased average order value thanks to AI-supported personalization of cross-selling and upselling offers.
- Improved workforce productivity and reduced labor costs due to the use of AI-enabled industrial robots.
- Streamlined and more accurate quality control with the help of computer-vision-based defect recognition.
- Low-risk planning of new manufacturing initiatives and real-time visibility of the entire production process via AI-enabled digital twins.
- Increased lifespan of manufacturing assets (machines, equipment, production lines) due to intelligent planning and optimization of asset use.
- Minimized risks of unforeseen disruptions due to AI-enabled predictive and prescriptive maintenance.
Oil and gas
- Optimized exploration strategies thanks to AI-based analysis of geological, seismic, and drilling data and suggestions on the best-performing oil and gas fields to develop.
- Digital twins for improved reservoir modeling.
- Streamlined operations control in offshore facilities and challenging environments due to advanced mapping and computer-vision-supported equipment diagnostics.
- Simplified environmental compliance thanks to real-time monitoring of the petroleum extraction process and instant spotting of potential risks (e.g., excessive methane emissions).
Engineering and construction
- Accurate planning of engineering projects and minimized construction risks thanks to AI-supported 4D/5D building information modeling (BIM).
- Streamlined control of an engineering project’s progress and improved worker safety due to remote computer-vision-based monitoring of the construction site and workers’ behavior.
- Lower operational costs thanks to AI-guided optimization of supply chain, asset use, and resource allocation.
- Simplified compliance with engineering quality standards thanks to real-time AI-based defect recognition.
Transportation and logistics
- Lower transportation costs thanks to AI-based recommendations on the optimal multi-modal shipment schemes and dynamic route optimization.
- Higher productivity of warehouse workers thanks to AI-guided order picking and packing.
- Improved driver safety due to real-time detection of non-compliant driver’s behavior and AI-based suggestions on safer driving conditions.
- The ability to leverage innovative transportation modes like autonomous vehicles, drones, and robots.
- Reduced business expenses and optimal human resource allocation thanks to AI-supported planning and optimization of servicing operations.
- Automated customer communication using intelligent chatbots and digital humans.
- Higher employee productivity thanks to fully automated intake and digital and paper document processing.
- Optimized coverage and energy efficiency thanks to AI-based planning of telecoms infrastructures.
- Minimized risk of outages due to AI-powered maintenance of telecom networks and equipment.
- Streamlined and faster field service operations thanks to AI-supported telecom equipment installation, diagnostics, and troubleshooting.
- Advanced analytics to accurately predict customer churn and loyalty, telemetry, fraud, and more.
- Fully automated metered billing and AI-supported debt collection.
- Personalized learning thanks to AI-supported creation of tailored educational paths.
- Faster submission analysis with the help of automated text recognition and natural language processing.
- Accurate knowledge assessment thanks to AI-based adaptive testing.
- Easier creation of versatile educational content using generative AI.
- Higher learning accessibility thanks to AI-enabled text-to-speech features and live translation of learning materials to multiple languages of choice.
- Optimized administrative processes due to AI-based curriculum planning, class scheduling, financial aid distribution, etc.
How to Develop Artificial Intelligence Software
Below, our experts share a typical roadmap we follow to deliver top-quality software with AI capabilities. The final scope and duration of each step depend on the complexity of your solution and the requirements for AI model performance.
Business analysis and feasibility study
- Analyzing the business needs and end users’ expectations that AI-powered software should meet.
- Eliciting functional and non-functional requirements for the software, including requirements for AI output accuracy (in different cases, satisfactory accuracy may be as low as 65% or as high as 95%), AI explainability (e.g., for legal compliance), data privacy, and model response time (stream or batch delivery of analytical results).
- Defining the project scope, estimating timelines and budget, and drawing a risk mitigation plan.
- Assessing the economic feasibility of AI implementation, calculating the NPV and ROI for the project.
AI-based software design
- Introducing a detailed list of features for AI-powered software.
- Designing the solution architecture and integrations between its components (AI and non-AI parts).
- Designing user-friendly UX and UI to interact conveniently with the AI software.
- Determining data feeds for AI models, defining the expected model outputs (e.g., forecasts on certain variables, prescriptions on optimal decisions), and planning the integrations with data sources and systems that will use analytical results.
- Selecting the fitting ML algorithms: supervised / unsupervised / reinforcement learning algorithms, CNNs, autoencoders, GANs, Bayesian deep learning, etc.
- Outlining initial model hyperparameters.
- Proof of concept delivery to prove the technical viability of the suggested software design.
Development and quality assurance
3.1. Non-AI part development
- Implementing DevOps: CI/CD pipelines, containerization tools, cloud automation, etc.
- Coding the server side of AI-supported software, including APIs for AI module integration.
- Creating role-specific user interfaces to interact with AI model outputs.
- Running all necessary testing procedures throughout the development process.
3.2. Data preparation
1–2 weeks (can be reiterated through the development to improve AI output)
- Consolidating data from relevant sources (proprietary historical data, third-party open-source and paid data sets).
- Performing exploratory data analysis to discover patterns and detect errors, outliers, biases, and anomalies.
- Data cleansing: standardizing, adding missed variables, removing duplicate and deviating information, anonymizing sensitive data.
- Splitting the prepared data into training, validation, and test sets.
3.3. AI model building and training
1–6 weeks (depending on the model’s complexity)
- Developing AI models, training each model on the training dataset, and matching the output against the validation dataset.
- Tuning the models’ hyperparameters to prevent under- and overfitting.
- Combining the most high-performing models into a single model to decrease the error rate of separate models.
- Evaluating the accuracy of the final ML model’s outputs against a test dataset in the pre-production environment.
Sometimes, developers reuse the same dataset to train, validate, and test an AI model. But it’s like assessing a student's knowledge by giving them the same questions they studied before an exam. This approach doesn’t help evaluate AI's actual ability to adapt to new input — if anything, it leads to insufficient accuracy in real-world scenarios. At ScienceSoft, we use separate datasets with the same probability distribution for AI training and evaluation to avoid AI bias reinforcement and eliminate the risk of inadequate validation.
Deployment and integration
- Configuring the AI solution’s infrastructure and implementing robust network security mechanisms.
- Deploying the AI and non-AI software parts to production and establishing connections between them.
- Integrating the solution with the required corporate and third-party systems.
- Setting the AI software live.
We typically start the AI software launch with pilot deployments to a limited number of users and run thorough user acceptance, integration, and compatibility tests. This way, we make sure the solution works well with the target software and infrastructure (incl. hardware) and can handle potential issues before a full-scale rollout.
Introducing a tailored AI adoption strategy (for enterprises)
Integrating AI in business-critical software may require organizational changes to ensure successful AI implementation and high adoption. ScienceSoft provides companies with practical assistance in:
- Upgrading corporate data policies to break down data silos across the departments, simplify access to data for the AI solution, and eliminate low-quality data that decreases AI output accuracy.
- Designing a plan to adapt employee workflows to the AI-powered software (involves planning time-framed AI launch initiatives and creating new role-specific policies).
- Drawing user tutorials and maintenance guides for in-house IT teams.
- Conducting employee training in a live, remote, or hybrid format.
Ongoing evolution and support
- Monitoring and optimization of AI-powered software performance.
- Prompt detection and fixing of AI-associated integration and security issues.
- Improving UX and UI based on user feedback.
- Continuous AI model tuning and regular model retraining for better accuracy.
- Upgrading the software with new AI-enabled features when needed.
AI maintenance is a separately controlled process focused mainly on fixing AI ‘drifts’ – decreasing model accuracy and increasing bias when the data used for analytics grows in volume and starts deviating from the initial training set. In the case of such drifts, our experts tune the hyperparameters and retrain the model on a new data set reflecting shifts in data patterns. Sometimes, replacing the model with a new, higher-performing one is a more cost-effective option than trying to adjust the old model to the drifts.
We create an optimal feature set, architecture design, and tech stack for your AI-based software and evaluate its economic feasibility. You also receive a detailed project plan and get expert advice on AI security and compliance to prevent AI implementation risks.
Developing software with AI capabilities
Our experts develop the non-AI software part, design and implement AI models, establish the required integrations, and run all necessary QA procedures. In 3–5 months, you receive an MVP of your tailored AI solution and can start generating early payback.
AI-based solution audit and revamp
We perform an expert audit of your AI-powered software to identify its operational issues and suggest cost-effective ways to fix them. Our experts can quickly upgrade the software and retrain your AI models for >95% output accuracy to help you drive higher value with your existing solution.
Why Choose ScienceSoft to Deliver Software Powered by AI
Over 30 years
in data science and AI
average experience of our IT professionals
Our tech partnerships and awards
Deep multi-domain expertise
- Since 2005 in business intelligence and data warehousing; since 2013 in big data and image analysis services.
- Seasoned project managers, business analysts, solution architects, data scientists, software engineers, and QA experts having practical knowledge of 30+ industries, including healthcare, BFSI, manufacturing, retail, and telecoms.
- A full-scale project management office to run enterprise-level AI implementation projects.
- In-house compliance consultants to achieve AI software compliance with HIPAA, PCI DSS, GLBA, GDPR, and other necessary regulations.
- Since 2003 in cybersecurity to ensure world-class protection of AI-powered software and data it operates.
Guaranteed service quality
- 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.
- A leading outsourcing provider featured on the Global Outsourcing 100 list by IAOP.
We are trusted by global market leaders
Typical Roles on Our AI Solution Development Teams
Analyzes business and end user needs and translates them into clear technical requirements for AI-powered software.
Designs secure and scalable architectures for the AI-powered software and the integration solutions.
Analyzes legal requirements for the AI-supported solution, advices on the proper compliance maintenance policies to implement.
Plans the project, manages the AI development life cycle, fosters collaboration between business and tech stakeholders.
Cleanses the data for AI and engineers solution features; builds, trains, tests, and validates ML models.
Creates wireframes, user journeys, and UI prototypes for AI-driven software following the principles of user-centricity.
Code the back end and APIs for AI-based software, create user interfaces, fix the bugs identified by QA experts, and further evolve the solution.
Designs and implements a test strategy, a test plan, and test cases to validate the quality and security of the AI software, reports on the QA results.
Deploys AI models in production, monitors their performance, and handles maintenance tasks.
Sourcing Models of Developing Software with AI Capabilities
- Full control over the project.
- Limited team scalability.
- High hiring and training costs to grow in-house AI competencies.
Turn to ScienceSoft to get a consultation on project planning, AI software design, model (re)training, and more.
- Fast access to the required AI and software development skills.
- Balanced project costs.
- High requirements for in-house project management to prevent collaboration issues.
Turn to ScienceSoft to quickly enhance your team with the required AI and software engineering skills.
ScienceSoft as a Reliable AI Consulting and Development Partner
ScienceSoft’s team undertook the development of our AI-based software product from scratch. […] They delivered software in time and with the required quality. The team regularly shared detailed project updates and recommendations on how certain functions could be implemented in a more efficient way.
Invention Machine Corporation
ScienceSoft has delivered cutting-edge solutions to complex problems bringing in innovative ideas and developments. ScienceSoft is dedicated to handling any problem that occurs as a result of hardware or software issues; simply put, they will go the extra mile to support their customers regardless of the time of day these issues arise.
Chief Operating Officer
ScienceSoft was to cover end-to-end development of an intelligent paperless invoice processing module. They selected and implemented techs that ensured the system’s availability and fault-tolerance in the future. We were particularly pleased with neat alignment of the developed module with our requirements.
We commissioned ScienceSoft to audit and upgrade our partially developed AI-based software for clay pigeon shooting tracking. The team identified core errors, which didn’t allow efficient solution operation, and implemented high-speed convolutional neural networks to fix them.
ScienceSoft's Tech Stack to Build AI-Powered Software
Cloud Services We Recommend to Speed Up the 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:
Some of the platforms’ distinctive features are outlined below:
- 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 transformation and visualization capabilities.
- A unified repository to store, organize, and reuse ML features.
- A marketplace with pre-built ML algorithms and models.
Enterprise-scale AI integration initiatives.
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.
Flexible AI deployment (on-premises/hybrid cloud).
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.
Integration of resource-intensive deep learning AI into software; startup-friendly.
Cost 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:
- The quality and volume of data used for AI and the number of data sources.
- Data type (unstructured data is more expensive to process than structured).
- Data origin (there may be a need to buy external data) and whether it needs labeling.
- Required accuracy rate for AI (the higher it is, the more time-consuming and expertise-demanding ML model tuning will be).
- The complexity of ML algorithms.
- Deployment type (AI outputs are in batches or in near-real-time).
- Infrastructure costs: cloud storage, security tools, etc.
An AI-powered solution that automatically extracts unstructured data from several sources, classifies it using an ML algorithm of moderate complexity, and provides outputs in batches.
A complex AI-powered solution that processes data of various types from a large number of sources and relies on advanced, expertise-demanding ML algorithms of high accuracy.
ScienceSoft is a global IT consulting and software development company headquartered in McKinney, Texas. For over 30 years, we help companies design and build state-of-the-art software enhanced with AI and ensure smooth solution performance in the long run. In our AI development projects, we employ robust quality management and data security management systems backed up by ISO 9001 and ISO 27001 certifications.