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
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.
Industry-Specific AI Benefits We Help Unlock
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.
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.
2.
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.
3.
Development and quality assurance
3.1. Non-AI part development
3–36 months
- 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.
4.
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.
5.
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.
6.
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.
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.
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
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.
- For the second straight year, ScienceSoft USA Corporation is listed among The Americas’ Fastest-Growing Companies by the Financial Times.
We are trusted by global market leaders
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.
Sourcing Models of Developing Software with AI Capabilities
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.
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
$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.
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.