ScienceSoft Global
Healthcare
Finance
We help companies identify where AI can create the most value — whether by improving internal operations, reducing costs, or enabling new product capabilities. Then, we define the right technical approach, including the choice between GenAI, ML, or hybrid architectures, model strategy, adaptation methods, and the required infrastructure, security, and governance. We also deliver a roadmap covering integrations, data preparation, guardrails, compliance, and rollout priorities to make AI work effectively with the client’s systems, workflows, data sources, or product environment.
Artificial Intelligence Consulting Services
AI consulting helps companies find where AI can deliver the most business value and cost savings beyond what traditional automation can achieve. At ScienceSoft, we identify high-impact use cases, design secure and cost-effective AI solutions, and integrate them with each client’s data sources, workflows, and technology ecosystem.
About ScienceSoft
Companies That Trust Us
How We Help
With 36 years of experience in AI and 750+ technology experts on board, ScienceSoft helps businesses plan, build, integrate, and evolve AI solutions that fit today’s operational realities. We work with both GenAI and traditional machine learning and deep learning, selecting the right approach based on business goals, risk profile, data readiness, and expected ROI.
PoC delivery
We create proof-of-concept solutions to validate AI feasibility before larger investments are made.
- For GenAI systems, we test output quality, grounding, guardrails, and safe use of internal or product data in realistic scenarios.
- For custom machine learning (ML) models, including deep learning (DL), we validate data suitability, expected model performance, and technical feasibility.
In both cases, the goal is to reduce delivery risk and confirm business value early.
MVP development
We build AI MVPs to help our clients launch faster, validate user adoption, and begin capturing value with lower risk. MVP delivery timelines typically start from 2+ months, depending on the scope and complexity of the solution.
- For GenAI, an MVP can be an assistant, copilot, voice agent, or agentic workflow for internal teams or end users.
- For traditional ML, the MVP scope can include forecasting, anomaly detection, classification, or computer vision capabilities embedded into business applications or commercial products.
We cover the full MVP development cycle, from architecture and data preparation to testing, integration, and production readiness.
AI software development
We build AI-powered software from scratch and add AI capabilities to existing applications and digital products. Our team develops:
- GenAI solutions, including chatbots, copilots, assistants, agents, and multi-agent systems.
- AI software that applies ML and DL for prediction, classification, detection, and optimization.
- Hybrid systems where GenAI handles natural-language interaction and unstructured content, while traditional ML delivers reliable predictions and decision logic.
We tailor AI software to each client’s domain, data, architecture, and security requirements.
AI model design and training
When off-the-shelf models are not enough, our data scientists design, train, and optimize custom AI/ML models for tasks such as natural language processing, image recognition, risk scoring, forecasting, and content generation.
We handle the full model development cycle, including data preparation, labeling, feature engineering, fine-tuning, training, validation, and performance optimization.
AI support, optimization, and audit
We help our clients continuously improve the quality, safety, and cost-efficiency of their AI solutions. Our services include:
- Monitoring AI output quality, usage patterns, and model or data drift.
- Optimizing prompts, retrieval pipelines, model settings, and inference costs.
- Refining guardrails, permissions, and approval flows for AI agents.
- Reviewing integrations, security controls, and compliance readiness.
- Auditing underperforming AI solutions and defining improvement measures.
AI Software and Capabilities ScienceSoft Specializes In
With hands-on experience across 30+ industries, ScienceSoft tailors AI solutions to the specific needs of each domain, including healthcare, insurance, lending, banking, manufacturing, retail and ecommerce, advertising, education, professional services, and more.
By AI capability
Generative AI
AI systems that generate, transform, or interpret content such as text, images, audio, video, or code. We build:
- Chatbots
- Copilots
- Agents, including voice agents
- Multi-agent systems
These solutions can be used for customer and employee support, knowledge search, document drafting, workflow orchestration, content generation, and task execution across business systems and digital products.
Machine Learning, including Deep Learning
AI systems that learn from structured data as well as images, audio recordings, and sensor readings to detect patterns, make predictions, and support decision-making. We build:
- Predictive AI
- Prescriptive AI and optimization engines
- Recommendation and personalization engines
- Anomaly detection systems
- Computer vision
- Descriptive and diagnostic AI
- Classical NLP
These solutions are used where measurable, repeatable outputs are critical, for example, in forecasting, fraud detection, scoring, quality control, image analysis, and operational optimization.
By business application
Customer service and support
- Virtual support agents, chatbots, and voice agents that handle customer requests in natural language, including domain-specific scenarios such as appointment booking, claim filing, and loan application assistance.
- AI assistants for human employees that generate summaries, suggest replies, and recommend next-best actions.
- Intent detection, request routing, customer sentiment analysis, and churn-related service analytics engines.
- Speech-to-text and text-to-speech capabilities for voice-based service experiences.
Financial management
- Staff copilots for financial reporting, commentary drafting, variance analysis, and policy or procedure guidance.
- Agentic workflows for reconciliations, exception handling, approval routing, and other finance operations tasks.
- Financial performance and risk forecasting software.
- Financial modeling tools.
- Prescriptive financial planning (budgeting, cost optimization, debt collection, risk hedging, M&O, etc.) tools.
- Financial fraud detection engines.
For financial services:
- Customer risk assessment and credit scoring software.
- Automated compliance checks.
- AI-supported decision-making software, e.g., on bank account opening, loan origination, mortgage, insurance claims, underwriting, and investment decisions.
- Debt collection management tools.
- Payment processing enhancement.
Diagnosing, treatment, and medical imaging
- AI tools for EHR workflows, clinical notes, appointment summaries, coding support, and structured data entry.
- Medical image analysis software for MRI, CT, PET, SPECT, X-ray, ultrasound, and other modalities.
- Clinical decision support, risk stratification, diagnostic assistance, and treatment recommendation support engines.
- Patient-facing chatbots and voice assistants for intake, scheduling, and follow-up communication.
- Remote patient monitoring software, including AI-enabled medical devices, cohort analytics tools, readmission risk prediction software, and AI support for long-term care.
Asset maintenance
- Technician copilots with RAG-based search across SOPs, manuals, and maintenance knowledge bases.
- Predictive maintenance, anomaly detection, early-failure identification, and remaining useful life forecasting tools.
- Automated summaries of equipment events, sensor changes, and incident patterns.
- AI recommendations for maintenance timing, asset utilization, lifecycle decisions, and energy optimization.
Production management
- Production planning, scheduling, and line optimization engines.
- Computer vision software for quality control, defect detection, and inspection automation.
- AI copilots for operators and supervisors, including SOP guidance, troubleshooting support, and incident summaries.
- Real-time performance monitoring tools.
- Energy consumption optimization engines.
- Procurement copilots for supplier comparison, RFQ drafting, and contract Q&A.
- Exception-handling agents for shipment delays, shortages, and substitutions.
- Demand forecasting, multi-echelon inventory optimization,, and supplier performance analytics.
- Transportation mode selection, route optimization, fleet monitoring, and warehouse assistance software.
- Inventory counting with computer vision
- Automated product inspection
Sales and marketing
- Audience segmentation, offer development, and A/B test design.
- Personalized content generation with brand and compliance guardrails.
- Meeting and call summarization with action items automatically captured in CRM.
- Lead scoring, deal prioritization, churn prediction, upsell recommendations, and dynamic pricing.
- Multi-dimensional customer segmentation, sentiment analysis, and personalized outreach automation.
Document management
- Enterprise knowledge assistance powered by RAG for document search, question answering, and internal knowledge access.
- Extraction, validation, and analysis of data from digital documents, including mixed-layout and semi-structured files.
- Paper-to-digital conversion, document classification, and intelligent routing.
- Automated drafting of business documents, reports, summaries, and customer communications.
HR management
- HR copilots for recruitment content generation, onboarding support, policy interpretation, and internal HR request handling.
- Employee assistants for benefits, leave, and knowledge-related questions.
- AI-powered CV screening, candidate matching, and recruitment bias detection tools.
- Turnover risk analysis, employee sentiment analysis, and personalized performance or learning recommendations.
Security and fraud detection
- SOC tools for alert triage, investigation summaries, and natural-language querying across security data.
- Detection of digital fraud, phishing, malware, and other suspicious behavior patterns.
- Detection of unsafe or fraudulent activity in physical environments using computer vision and access-log analysis.
- Biometric-based authentication and AI-assisted security monitoring.
Conversational AI Assistant for Patient Appointment Scheduling
See how conversational AI can be used to automate complex interactions like patient appointment scheduling. The AI voice agent, based on Amazon’s Nova Sonic Speech-to-Speech model, uses real-time speech recognition, identity verification, and system integration to handle patient appointment scheduling end-to-end without human intervention. Scalable across tasks and industries, the agent has the potential to reduce operational costs in customer engagement by 50%.
Agentic AI for Transforming Investment Decision-Making
See how ScienceSoft’s Investment AI Agent leverages predictive analytics, NLP, and knowledge graph reasoning to uncover market insights and support smarter investment decisions. Built on the LangChain framework and powered by OpenAI’s best-in-class LLMs, the agent streamlines research workflows, boosts analyst productivity by over 50%, and speeds up investment analysis by up to 70% while ensuring high-precision investment recommendations.
Voice AI Agent for Insurance Claim Validation
See how ScienceSoft’s Insurance AI Agent applies voice intelligence and advanced sentiment analysis to validate submission accuracy and uncover fraud attempts during conversational claim verification. Developed on the AWS Bedrock AgentCore framework and powered by OpenAI’s leading LLMs, the agent boosts investigator capacity by over 40% and drives 20%+ higher fraud detection rates through nuanced, call-based discrepancy indicators.
Our Clients Say
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 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.
They delivered a fully customized AI medical chatbot PoC in just two weeks, which was unbelievable.
The attention to detail in the chatbot design, and especially the pitch deck, was amazing — with this kit on hand, we are ready to go into investor discussions confidently. It’s not often that you find a team that moves this fast without sacrificing quality.
What Makes ScienceSoft a Reliable AI Software Consulting Company
- 37 years in AI and software engineering.
- 750+ experts, including AI, software engineering, security, and QA specialists, backed by an Architecture & Solutions Center of Excellence. Over 50% of our developers are senior and lead specialists.
- Proven delivery discipline backed by 4,200+ successful projects and an in-house PMO.
- Experience in designing, training, and fine-tuning custom AI models, with case studies demonstrating 95%+ accuracy in specialized use cases.
- Domain and compliance expertise in 30+ industries, including healthcare, insurance, lending, manufacturing, retail, and telecoms.
- Strong security and quality management practices backed by ISO 27001 and ISO 9001 certifications.
Our awards, certifications, and partnerships
Named among America’s Fastest-Growing Companies by Financial Times, 5 years in a row
Listed in IAOP’s Global Outsourcing 100 for the 5th year running
Semifinalist in Amazon Nova Partner Demo Competition for real-time AI voice scheduler
HTN Now Awards 2025/26 Finalist in Best AI Scribe Solution Category
Microsoft Solutions Partner for Data & AI
AWS Partner since 2017
ISO 9001-certified quality management system
ISO 27001-certified security management system
AI Service Costs
Pricing models ScienceSoft uses
Time and Materials
Best for:
- Agile development of AI-based solutions.
- Continuous consulting during AI implementation.
- Iterative AI software evolution, reengineering, bug fixing.
Fixed price
Best for:
- One-time consulting activities (e.g., AI solution architecture design, code audit, project planning).
- Feasibility study and PoC development.
- Fixed-scoped tasks (e.g., building a particular AI-supported feature, AI model development, one-time AI solution testing).
Subscription fee
Best for:
- AI infrastructure management (data center management, AI software configuration, cloud resource optimization, etc.).
- Managed AI security and compliance.
- L1–L3 help desk (a per-ticket model applies as well).
Sample cost ranges of AI consulting
$15,000
AI product consulting for startups.
$6,000–$140,000
Consulting on an enterprise AI solution:
- Ideation and feasibility study: $6,000–$24,000
- Implementation: $18,000– $140,000
- Evolution: $24,000/year– $140,000/year
Sample cost ranges for AI development
$10,000–$100,000
A compact AI capability with a narrow scope, such as an FAQ chatbot, document search assistant, summarization tool, data extraction component, or voice interface. The cost depends on the number of data sources, the level of autonomy, integration needs, and whether voice or real-time interaction is required.
$50,000–$150,000+
An AI workflow or agent that performs multi-step tasks and interacts with business systems or product logic, for example, to triage new cases, schedule appointments, or recommend next best actions. The cost depends on workflow complexity, integrations, guardrails, and model monitoring requirements.
$100,000–$300,000+
A full AI application or a substantial AI module within a product, with multiple user flows, integrations, and supporting logic, where AI is a core part of the experience rather than a single feature. The cost depends on UX scope, backend integration complexity, model adaptation, and production-readiness needs.
$300,000–$1,000,000+
A large AI-enabled platform or enterprise-grade product with several AI components (copilots, agents, analytics, and decision-support tools) working together across workflows, roles, or business domains. The cost depends on the breadth of functionality, data architecture, security and compliance requirements, and rollout scale.
AI Technologies We Use
Generative AI
Models
Model adaptation and efficiency
AI platforms and services
Agents and Orchestration
Traditional ML
Platforms and services
Frameworks and libraries
Programming languages
Big data
Data visualization
For many AI initiatives, it is faster and more cost-efficient to start with proven pre-trained models and adapt them to the use case than to build a model from scratch. In GenAI projects, this adaptation may involve prompt engineering, RAG, fine-tuning, or agentic orchestration; in classical ML, it may mean selecting and tuning an existing architecture for the available data and performance requirements. As adoption grows, the key decision often shifts from “Which model is the best and most accurate?” to “Which model gives us the right quality at the right cost?”
Fully custom models are usually justified only when the business requires exceptional accuracy, tighter latency control, and stronger data isolation, for example, in medical diagnostics, fraud detection, credit scoring, or manufacturing quality control.
AI Use Continues to Broaden
AI adoption is growing fast. McKinsey’s 2025 global survey found that 88% of organizations already use AI in at least one business function, up from 78% a year earlier. But at the enterprise level, the majority are still in the experimenting or piloting stages, with only approximately one-third reporting that their companies have begun to scale their AI programs. According to the same survey, there is strong interest in AI agents, with 62% of respondents saying their organizations are at least experimenting with them. By industry, the use of AI agents is most widely reported in the technology, media and telecommunications, and healthcare sectors.
According to Deloitte’s 2025 AI survey, AI investment is also rising across industries, with 91% of organizations planning to increase AI spending in the next 12 months. So, the question is no longer whether to invest in AI, but how to invest in the right use cases and implementation model.
ScienceSoft’s Approach to Artificial Intelligence Consulting
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Identify high-value AI opportunitiesWe start by analyzing the client’s enterprise processes or digital products, pain points, data landscape, and constraints to identify where AI can bring the greatest measurable value or cost reduction. At this stage, we focus on cases where traditional automation is not enough or where AI can enable new capabilities, and determine whether GenAI, traditional ML, or a hybrid approach is the best fit. When feasibility or expected ROI needs to be validated before full-scale implementation, we recommend a focused PoC to test the concept, estimate business value, and reveal key risks early. |
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Choose a modern, cost-effective AI stackWe define the technical approach that best fits the use case, budget, and risk profile. This includes decisions such as:
Where required, we also balance output quality, scalability, and explainability to ensure the AI solution meets both business and regulatory expectations. |
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Prepare data and integrationsWe assess what data is needed, how ready it is, and how it should be prepared for the selected AI pattern. For RAG systems, this may include content cleanup, validation, deduplication, metadata standardization, indexing, and permission-aware retrieval. For ML systems, this may include data quality checks, labeling, enrichment, and train/validation/test set preparation. We also define how the AI component will connect to surrounding applications, databases, and workflows. |
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Establish safety, security, and compliance guardrailsWe design AI solutions to operate safely within the client’s ecosystem. Depending on the use case, this includes role-based access, approval flows for agent actions, audit trails, prompt-injection defenses, output constraints, monitoring, and compliance controls aligned with applicable regulations and internal policies. When personal data is involved, we also account for transparent data handling and consent-based processing where applicable. To protect data processing and storage, we embed security practices into delivery and operations using a DevSecOps approach. |
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Validate quality and business impactTo make sure the AI solution remains viable in practice, we define quality and business KPIs early and check the solution against them throughout implementation and evolution. This helps confirm not only technical correctness, but also the relevance, safety, speed, and measurable business value of AI outputs, predictions, recommendations, and actions. |
Insights From ScienceSoft's Experts
Hadeel Abu Baker
Senior Healthcare IT & AI Consultant, ScienceSoft
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Q1 2026 Healthcare AI Trends by ScienceSoft | March 2026
Vital Soupel
Senior Insurance IT & AI Consultant, ScienceSoft
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Q1 2026 Insurance Artificial Intelligence Trends | Apr 2026
Mary Zayats
Head of Technology and Competency Development, Financial IT Principal Consultant, ScienceSoft
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Q1 2026 Investment Artificial Intelligence Trends | Apr 2026
Featured Expert Talks
AI Agents First: Operating Model for Healthcare Contact Centers | Presentation at WHX Dubai 2026
Presentation by Hadeel Abu Baker, Senior Healthcare IT Consultant at ScienceSoft. Hadeel explains how AI in contact centers can complete patient requests end to end and how the AI-first operating model can improve patient access, reduce manual workload, and scale service quality.
AI Agents for Insurance Claims Fraud Detection | Presentation at ITS 2025
Presentation by Vadim Belski, Head of AI and Principal Architect at ScienceSoft, from the 2025 Insurance Transformation Summit in Boston. Vadim explores the potential of agentic AI in insurance and demonstrates how voice AI agents can streamline conversational claim verification and enhance fraud detection through voice intelligence and advanced sentiment analysis.
ScienceSoft in Industry Media
From Assist to Resolve: Multi-Agent Model for AI-First Healthcare Contact Centres
by Hadeel Abu Baker, Senior Healthcare IT Consultant at ScienceSoft
March 23, 2026
Deploying Agentic AI for Insurance Fraud Detection: A Practical Look
by Vadim Belski, Head of AI, Principal Architect at ScienceSoft
November 25, 2025
How AI Transforms the Mortgage Lending Industry
by Natallia Babrovich, Financial and Banking IT Consultant
February 17, 2025
