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AI consulting services & expertise
How we can help
Business case development
We analyze your corporate workflows and existing systems to identify process bottlenecks or areas of inefficiency that can be addressed with AI. Based on these findings, we create a list of potential AI use cases and prioritize them based on their business impact and implementation complexity.
AI readiness assessment
Our team assesses your organization’s readiness to implement the top use cases on the list, taking into account your in-house expertise, IT ecosystem, data quality and its availability, and other key aspects.
AI strategy development
Based on the short-term and long-term business goals, priority of use cases and business readiness for it, we choose one or more use cases for implementation, establish goals and KPIs, select technologies and design the solution’s architecture, and define the project’s budget and timeline.
AI adoption
Our experts provide oversight on the launch of AI pilot projects and their scaling into successful AI solutions, assisting with key aspects like KPI tracking, data governance, risk and change management, and responsible AI.
Our key AI expertise
Automating complex, multi-step business tasks, data analysis and management, and customer service operations to reduce employee workload and boost operational efficiency.
Providing employees and consumers with competent 24/7 assistance with their questions and tasks to foster workforce productivity and improve customer experience.
Generative AI solutions
Creating new content, including text, images, audio, or code, in real time to speed up administrative and creative workflows and enable personalization at scale.
Analyzing visual data from digital content or real-world environments to extract actionable insights or trigger specific actions in software systems or smart equipment for business process automation.
Processing large sets of real-time and historical data to forecast future business outcomes, enabling data-driven decision-making and helping optimize your business strategies.
AI infrastructure & MLOps
Automating and streamlining AI model deployment, monitoring, and fine-tuning to ensure optimal software performance and simplify the evolution of your AI solution in line with changing business needs.
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Client spotlight
Medical copilot for mental care
Business case
Our client, a US-based psychological care provider, was experiencing serious inefficiencies in its patient data management processes. This resulted in complex patient recordkeeping and made it difficult to retrieve information on past visits and cases.
How we helped
- Identified three time-consuming activities with high potential for AI-driven automation: visit transcription, patient data entry, and data retrieval.
- Conceptualized a medical copilot that can generate a full transcript of each visit, enter the information collected in the patient’s EHR in a suitable format, and answer physicians' questions on past visits in natural language.
- Recommended building the solution on top of Amazon Bedrock rather than developing a custom one from scratch to mitigate upfront costs and ensure faster delivery.
- Launched a pilot program with a select group of clinicians to test the copilot's accuracy in transcription and query responses.
- Oversaw the pilot’s company-wide roll-out since the solution met the set KPIs and provided training to all involved medical staff.
Results
- Pilot launched 2 months after the project’s start followed by a complete solution rollout 6 weeks later
- 20% reduction in administrative workload for doctors
Computer vision defect detection
Business case
A major plywood manufacturer was inquiring into ways to adopt AI into their processes to improve their operational efficiency.
How we helped
- Assessed the manufacturer’s business processes, identified manual quality control workflow as slow, inconsistent, and error-prone, and recommended automating it with AI.
- Deemed this use case feasible due to the high availability of data, such as thousands of classified plywood panel images from monthly quality controls.
- Conceptualized a computer vision solution powered by neural networks for defect detection and classification.
- Suggested integrating the AI solution with high-speed cameras and deploying it on an edge computing device directly on the conveyer belt to minimize classification latency.
- Launched a pilot project on a single production line, focusing on minimizing false positives and negatives and helping resolve any uncovered issues.
- Supervised the final solution rollout across the plant, conducting user training, advising on business process change to accommodate AI adoption, and making sure the project met the initially established targets.
Results
- 80% increase in defect detection and sorting speed
- 19% reduction in product return rate due to improved classification accuracy
AI agent for an online store
Business case
An online retailer selling construction materials was struggling with the high volume of consultation inquiries and decreasing customer loyalty due to delivery delays.
How we helped
- Identified two core bottlenecks that could be addressed with AI: repetitive product information requests overwhelming the sales team and manual routing of a mounting number of delivery orders resulting in poor logistics performance.
- Confirmed data availability for AI solution training, including the current product catalog, support ticket logs, and historical GPS delivery data.
- Suggested creating a GenAI-powered agent for real-time product consultation and a route planning AI agent for fleet mileage optimization to help the client make customer service and logistics processes more scalable.
- Recommended enhancing the consultation agent with Retrieval-Augmented Generation to provide accurate responses based on the client’s knowledge base, as well as implementing a validation mechanism that enables human planners to confirm or adjust AI-generated routes.
- Helped launch a pilot, making the agent a full-time member of the sales and logistics team that handles customer inquiries and creates delivery routes under the supervision of a dedicated employee.
- Launched a full-scale AI agent solution, setting it up to handle all incoming inquiries and escalating them to human agents only if necessary, while also automating all delivery routing tasks.
Results
- 35% customer support dialogues fully automated and 40% automated partially
- 12% lowered logistics costs for the same volume of orders
Why Itransition
Providing IT services and software solutions since 1998
5+ years of experience in AI consulting and development
In-house AI/ML Center of Excellence
Official partnerships with Microsoft and AWS
Quality and information security management compliant with ISO 9001 and ISO 27001
Clients ranging from startups to Fortune 500 companies
Awards and recognitions from Deloitte, Gartner, Forrester, and Everest Group
4.9 overall review rating on Clutch
Artificial intelligence consulting
for various industries
Itransition’s consultants help identify the most suitable AI use cases based on your industry and business scenario and facilitate their successful implementation to get the expected ROI.
Retail
Education
Financial services
Transportation
Automotive
Healthcare
Agriculture
Manufacturing
Real estate
Logistics & supply chain
Transform your business processes with Itransition’s AI solutions
Itransition’s approach to AI consulting
We prioritize the following critical aspects to ensure the AI initiative’s success and avoid any implementation missteps.
Data quality & availability
Our experts evaluate your corporate data quality and availability, and if these datasets can’t support your future AI solution’s proper operation, we guide you through reorganizing and cleaning them to ensure their reliability.
Regulatory compliance
We help ensure AI systems' strict compliance with applicable ethical standards and international regulations, including GDPR, HIPAA, and others to ensure responsible AI use and safeguard user data.
AI model explainability & accuracy
We ensure careful data preparation, bias-free model training, and transparent technical specifications to make your AI tool reliable and auditable and at least partially overcome the black-box nature of AI.
Streamlined deployment
Our AI experts use their expertise to minimize trial-and-error, speed up data preparation, choose the right tools, effectively mitigate technical and business risks, and create an optimal development and adoption roadmap, fast-tracking the delivery of even complex AI initiatives.
Extensive risk analysis
Drawing on a thorough analysis of your business processes, as well as our rich expertise in AI development for various industries, we uncover and account for all technical and business-related risks of AI adoption that are relevant to your business case and provide an actionable plan to successfully overcome them.
FAQs
AI consultants and consulting firms offer their holistic expertise to help companies ensure the success of artificial intelligence projects, derive maximum business value from using AI technology, and thus gain a competitive advantage. They can help stakeholders elaborate a comprehensive AI strategy, conceptualize the most relevant AI use cases for your business model, assess your company’s readiness for AI-driven digital transformation, and oversee an AI application’s adoption to ensure the project’s success.
The budget for an AI implementation project can vary widely. The main cost factors include the type of project (custom AI development, AI integration into existing systems, etc.), the AI-related expertise required (data science, natural language processing or NLP, deep learning, etc.) the solution’s AI capabilities, and data requirements (data types, volume, and quality). Other key aspects to consider are the software’s architecture, tech stack (AI platforms, frameworks, algorithms, etc.), product licensing costs, deployment model, and solution maintenance needs.
The list of experts and professionals involved typically includes a project manager, business analysts, data scientists and data engineers, machine learning engineers, UI/UX designers, front-end and back-end developers, QA engineers, DevOps engineers, and support engineers.
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