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.
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Head of Data Analytics Department, ScienceSoft
Head of PMO, ScienceSoft
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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 business needs (for enterprises); develop the competitive advantage framework (for software product companies).
- Choose between proprietary AI model development and pre-trained model integration.
- Design the solution architecture in line with performance and scalability requirements.
- Design UX/UI with future solutions users in mind.
- Collect data for a proprietary ML model creation and split the data into training, validation, and test sets. In the case of pre-trained model integration, customize it if necessary.
- Perform exploratory data analysis, train, and fine-tune the model.
- Code the server side of the solution and perform in-parallel testing.
- Deploy the proprietary model within the software or integrate the pre-trained one. Scale and optimize the model according to the demonstrated results.
- Integrate the solution with corporate third-party systems (if applicable) and UI (a web page, an analytics dashboard).
- Introduce a corporate user adoption strategy (for enterprises) and ensure continuous solution evolution and maintenance to preserve accurate AI output and proactively fix arising issues.
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.
Industry-Specific AI Benefits
Healthcare
- 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.
Banking
- 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.
Financial services
- 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.
Insurance
- 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.
Manufacturing
- 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.
Professional services
- 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.
Telecommunications
- 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.
Education
- 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.
1.
AI-powered app concept elaboration and product design
- For enterprises: analyzing the existing infrastructure and processes to be enhanced with AI; TCO calculation; analyzing the business needs and end users’ expectations.
- For software companies: building the competitive advantage framework (e.g., identifying the target audience and major competitors, outlining app features crucial for winning the target audience, creating user personas and mapping their user journeys, building a go-to-market strategy).
- Defining functional and non-functional requirements for the solution, including exact AI capabilities, compliance with data privacy regulations, solution compatibility with other systems, etc.
- Defining the project scope, estimating costs and timelines, and creating a risk mitigation plan.
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2.
Deciding on the optimal approach to the future ML model
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Choosing the available pre-trained ML model most suitable to meet the required needs (e.g., a GPT model, a model from the SpaCy library, PyTorch Hub, etc.), considering the use case and goal, cost, data requirements, model complexity, how well-trained and scalable it is, etc.
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Creating a proprietary ML model for non-standard, highly innovative or experimental use cases (including designing its architecture, training and optimizing the algorithm, hyperparameter tuning, cross-validation, and regularization).
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3.
AI-powered software design
- Designing architecture, software backend, and integrations.
- User-friendly UX and UI design for the convenience of end users.
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4.
ML model development and implementation
- Data collection for ML model training and evaluation (internal company data, open-source and paid third-party data sets).
- Performing exploratory data analysis (EDA) to identify patterns in data and discover inconsistencies like errors and anomalies.
- Data cleansing (standardizing, replacing missing and deviating variables, anonymizing sensitive data) and labeling.
- Splitting the prepared data into training, validation, and test sets.
- Training and fine-tuning the model.
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5.
Non-AI part development
- DevOps implementation: CI/CD pipelines, cloud automation, containerization tools, etc.
- Coding the server side of the AI-powered solution, including APIs for integration with other systems.
- Performing the required testing procedures throughout the development process.
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6.
Deployment and integration
- Putting the ML model into action on live data within the solution to provide actionable output.
- Error handling and exception handling in the software to manage situations where the model encounters unexpected input or fails to provide a meaningful output.
- Configuring the infrastructure of the AI-powered software and implementing reliable network security mechanisms.
- Deploying the software with the integrated model to the target environment.
- Testing and validating the integrated model.
- Scaling and optimizing the model to ensure that the deployed model can handle the expected workload efficiently.
- Integrating the solution with corporate and third-party systems (if applicable).
- Performing integration with the UI (e.g., a web page, a customer portal, analytics dashboards, etc.).
- Testing the entire solution.
- Setting the AI solution 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.
7.
Introducing a tailored AI adoption strategy
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.
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8.
Continuous solution evolution and optimization
- Monitoring and optimization of AI-powered solution performance.
- Promptly detecting and fixing AI-related security and integration issues.
- Adjusting UX and UI based on user feedback.
- Fine-tuning and retraining the ML model for better accuracy.
- Adding new AI features if 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.
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.
Sample costs
From ScienceSoft's experience, the cost of AI software development may vary from $100,000 to $650,000+, depending on solution complexity.
$100,000–$200,000
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.
$500,000–$650,000+
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.
AI-Based Software Development Cost Estimation
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Sourcing Models of Developing Software with AI Capabilities
Why Choose ScienceSoft to Deliver Software Powered by AI
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.
- ScienceSoft is a 3-Year Champion in The Americas’ Fastest-Growing Companies Rating by the Financial Times.
We are trusted by global market leaders
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.
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. Our collaboration was straightforward and efficient, and we would certainly recommend ScienceSoft as a reliable service provider.
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.
Examples of AI-Powered Solutions ScienceSoft Develops
Advanced analytics
- Intelligent root cause analysis.
- Trend and anomaly identification.
- Forecasting quantitative values and events.
- Analytics-based object clustering and segmentation.
Recommendation engines
- 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.
AI-powered chatbots
- Processing written and spoken human requests.
- Real-time generation of relevant responses.
- Human-like textual and voice communication.
Generative AI
- User prompt processing and real-time conversation.
- Producing new forms of creative content (text, images, audio, video, and more) based on specified parameters.
Software Powered by AI: ScienceSoft’s Success Stories
Typical Roles on Our AI Solution Development Teams
Business Analyst
Analyzes business and end user needs and translates them into clear technical requirements for AI-powered software.
Solution Architect
Designs secure and scalable architectures for the AI-powered software and the integration solutions.
Compliance Consultant
Analyzes legal requirements for the AI-supported solution, advices on the proper compliance maintenance policies to implement.
Project Manager
Plans the project, manages the AI development life cycle, fosters collaboration between business and tech stakeholders.
Data Scientist
Cleanses the data for AI and engineers solution features; builds, trains, tests, and validates ML models.
UX/UI Designer
Creates wireframes, user journeys, and UI prototypes for AI-driven software following the principles of user-centricity.
Software Developers
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.
QA Engineer
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.
Data Engineer
Deploys AI models in production, monitors their performance, and handles maintenance tasks.
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:
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Some of the platforms’ distinctive features are outlined below:
Amazon SageMaker
Description
- 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.
Best for
Enterprise-scale AI integration initiatives.
Azure Machine Learning Services
Description
- 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).
Google AI Platform
Description
- 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.
Trust the Development of AI-Powered Software to Professionals
Having 34 years of experience in software development and data science, ScienceSoft has all it takes to deliver effective, high-performing software with AI capabilities.
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.
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.
About ScienceSoft
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.