Artificial Intelligence Consulting Services
For over 30 years, ScienceSoft helps companies bridge the gap between the high-level vision of AI software and its actual implementation. We provide all-encompassing assistance across AI solution design, planning, development, and evolution to bring businesses to the new heights with the help of the cutting-edge AI technology.
Artificial intelligence (AI) consulting is a way for companies to get their AI-related business ideas and needs translated into an elegant technology solution and seamlessly implemented in reality.
As an experienced AI consulting partner, ScienceSoft designs state-of-the-art solutions fully tailored to its clients’ unique needs and figures out the ways to speed up AI development, ensure optimal project cost and minimized risks.
What Makes ScienceSoft a Reliable AI Company
- Data science and data analytics expertise since 1989.
- Business intelligence and data warehousing services since 2005.
- Big data and image analysis services since 2013.
- Domain experience in 30+ industries, including financial services, healthcare, manufacturing, retail, telecoms.
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AI consultants and developers with 7–20 years of relevant experience and competencies in major ML technologies, frameworks, and libraries.
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Expertise in industry-specific standards (HIPAA, GAMP, PCI DSS, GLBA, etc.) to create compliant AI solutions.
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ISO 9001 and ISO 27001-certified to ensure high quality of AI services and full security of the customers’ data.
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DevSecOps approach to create a strongly protected environment for data processing and storage.
Your benefits from partnering with ScienceSoft
End-to-End AI Consulting Services by ScienceSoft
The scope of ScienceSoft’s AI consulting services varies depending on our customers’ business needs, maturity of the data management practices, and the current AI environment. Our AI services may include:
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Business analysis
- Definition and analysis of business needs to be pursued with an AI solution.
- Analysis of the existing AI environment (if any), data management practices established within the company, the currently used technologies and tools.
2
AI solution conceptualization and development project planning
- Designing the architecture and feature set of the AI solution and creating the optimal tech stack for AI implementation.
- AI project planning: deliverables and KPIs, cost and timeline estimates, a risk management strategy, etc.
- AI solution PoC delivery (optional).
3
Data preparation management
- Exploratory analysis of the existing data sources (data type, volume, etc.).
- Data gathering and cleansing (standardizing, replacing missing and deviating variables, anonymizing sensitive data) for ML model training.
4
AI solution development (ML modeling)
- ML model refinement, testing and evaluation.
- Fine-tuning the parameters of ML models until the generated results are acceptable.
- Deploying the ML models.
5
ML output integration
- Development of application modules that use ML output.
- AI solution integration with the modules to feed the ML models and use ML output.
6
AI solution quality assurance
- End-to-end AI system testing.
- Evaluating the quality of AI solution output according to pre-defined KPIs.
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User training
- AI solution user training in the prefered format.
- Designing user guides and manuals and sharing them with the company's in-house IT team.
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AI solution support and optimization
- Continuous AI solution monitoring and optimization.
- Adding new data sources to feed the AI solution for greater accuracy and insight.
- Building new ML models to meet the newly arisen business needs.
AI Solutions ScienceSoft Builds
According to PwC, top 3 AI solutions that bring substantial value are increasing productivity through automation, improving decision-making, and improving customer experience. The solutions that ScienceSoft delivers are in line with the general picture:
Demand forecasting and inventory optimization
- Calculating the risks of out-of-stock and overstock.
- Sales and demand forecasting.
Value you get: supply-demand balance, minimized supply chain risks.
Predictive and prescriptive maintenance
- Flagging anomalous behavior of machinery and equipment.
- Predicting failure probability over time/in a certain number of steps.
- Predicting remaining useful lifetime.
Value you get: improved OEE and productivity, decreased maintenance and repair costs, increased asset lifetime and uptime.
Customer service
- Virtual customer agents.
- AI-powered recommendations for next-best actions for human agents.
- Speech-to-text conversion.
Value you get: enhanced rates of customer requests resolution, reduced customer service labor costs, increased customer satisfaction and loyalty.
Financial risk assessment
- Calculating and monitoring financial risks based on the analysis of customer risks, operational risks, geopolitical risks, and more.
- Financial risk forecasting.
- Prescriptive analytics to mitigate financial risks.
Value you get: minimized financial losses, increase in business profitability.
Personalization engines
- Customer segmentation.
- Cross-selling and upselling recommendations.
- Automated generation of individualized content – product recommendations, special offers and pricing, etc.
Value you get: automated marketing efforts, increased conversion rates and average order value, decreased cart and browse abandonment, etc.
- Internet of Medical Things.
- Industrial robot simulation software.
- Connected consumer products.
- Smart cities, etc.
Value you get: enhanced operational efficiency, automated remote control, object tracking capabilities, etc.
Speech recognition
- Voice-controlled navigation and search systems in vehicles.
- Virtual assistants.
- Voice search.
- Voice-based authentication in security systems.
Value you get: enhanced customer experience, reduced customer service labor costs, etc.
- Face and emotion recognition.
- Product quality control (checking for surface defects, discoloration, absence of components, etc.).
- Computer-aided diagnostic assessment.
- 3D reconstruction, etc.
Value you get: cost and time savings due to the automated processing of large amounts of visual data.
ScienceSoft’s Approach to Artificial Intelligence Consulting
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Data protectionWhen building AI-powered solutions, we apply the three-pillar approach to effective data security management:
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Data qualityTo enable high data quality, we engage certified data engineers and data scientists who employ a wide set of tools to automate data validation, cleansing, reduplication processes. |
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Guaranteed value of the AI solutionTo ensure the viability of your future AI solution, the speed and quality of the AI-enabled insights, we:
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Our Customers Say
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
AI consulting
We introduce a custom AI launch strategy, a clear concept of your AI solution, and an optimal tech stack for AI implementation. You also receive a detailed project plan with cost and time estimates and get expert advice on AI security and compliance.
AI implementation
We develop your AI solution end to end, from design to data preparation to AI model training and tuning to QA. We also integrate the AI software into your existing IT system and can provide after-launch support. You promptly get top-quality AI solution tailored to your business needs.
AI evolution
We revamp the architecture, code, and tech stack of your existing AI solution and upgrade the software with new capabilities. You get a modern AI solution that can be flexibly scaled to support sustainable business growth quickly and with minimal investments.