Machine learning services & solutions
About Itransition
5+ years of experience in machine learning consulting and solution delivery
25+ years in IT consulting and software development services
Dedicated AI/ML Center of Excellence
Delivering ML solutions compliant with HIPAA, GDPR, FDA, and other standards and regulations
Standing partnerships with Microsoft and AWS
Microsoft Azure AI Platform specialization holder
Clients across 40+ countries, including startups and Fortune 500 enterprises
Holding ISO 9001, ISO/IEC 27001, and ISO/IEC 15408 certifications to guarantee service quality and compliance
Client spotlight
19%
annual revenue growth
AI solution for defect detection
We implemented an AI solution for detecting defects in the produced medical packaging’s coating, helping the customer validate 100% of their products and improve the defect detection rate on the production line.
25%
client satisfaction increase
AI-powered shoppable video platform
We integrated ML and computer vision capabilities into a video ecommerce platform to enable automated product recognition across more than 1.5 million images and videos.
+8%
buyer conversion rate
AI-powered platform for a fashion retailer
We created a solution to conduct online user behavior analysis with ML to personalize user interactions and increase the visitors-to-buyers conversion rate.
19%
annual revenue growth
AI solution for defect detection
We implemented an AI solution for detecting defects in the produced medical packaging’s coating, helping the customer validate 100% of their products and improve the defect detection rate on the production line.
25%
client satisfaction increase
AI-powered shoppable video platform
We integrated ML and computer vision capabilities into a video ecommerce platform to enable automated product recognition across more than 1.5 million images and videos.
+8%
buyer conversion rate
AI-powered platform for a fashion retailer
We created a solution to conduct online user behavior analysis with ML to personalize user interactions and increase the visitors-to-buyers conversion rate.
19%
annual revenue growth
AI solution for defect detection
We implemented an AI solution for detecting defects in the produced medical packaging’s coating, helping the customer validate 100% of their products and improve the defect detection rate on the production line.
25%
client satisfaction increase
AI-powered shoppable video platform
We integrated ML and computer vision capabilities into a video ecommerce platform to enable automated product recognition across more than 1.5 million images and videos.
+8%
buyer conversion rate
AI-powered platform for a fashion retailer
We created a solution to conduct online user behavior analysis with ML to personalize user interactions and increase the visitors-to-buyers conversion rate.
Our customers say
Over the course of our collaboration with Itransition, we were consistently impressed with both skill and dedication their team employed to fulfill our business needs. Itransition’s involvement extended beyond the technical realization of the project, they acted as consultants, continuously helping us hone the project vision and suggesting approaches that would be best suited for the intricacies of our business.
Looking for top machine learning experts for your project?
Our machine learning services
Our AI researchers and data scientists offer end-to-end assistance for organizations that implement machine learning solutions or upgrade existing machine learning applications. Our services range from business needs analysis and designing ML models to selecting the optimal strategy and technologies for implementing ML.
We develop scale-ready MVPs and enterprise-wide ML tools to help our clients solve business problems and gain an advantage over their competitors. Our services include ML requirements elicitation, data management and visualization, machine learning model development and tuning, integration, and maintenance.
Our service delivery pipeline
1
Business needs analysis
We hold discovery workshops, interviews, and process observations to elicit business needs and user expectations, and assess the client’s technical environment. Based on the results, we select the suitable ML use case and define the ML solution’s scope and functional and non-functional requirements.
Basic team composition:
- A business solution consultant
- An ML solution architect
2
Initial data analysis
We carry out an exploratory analysis of the available data sources, both owned by the customer and from public databases, required to implement the project. After that, we conduct data cleansing, assist with data imputation or dimension reduction, design a data pre-processing pipeline, and guide data analysis flow creation.
Basic team composition:
- An ML solution architect
- A data scientist/ML engineer
3
ML solution design
In line with the elicited business needs, we design the architecture of the ML solution, define an implementation strategy, suggest optimal AI techniques, machine learning algorithms, and draw up a tech stack that can include both open-source and licensed software. If we need to deliver a PoC, we also outline its scope and optimal approach. Additionally, we define the timeline and budget for the project.
Basic team composition:
- A business solution consultant
- An ML solution architect
4
Building the ML solution
Our software engineers perform data pre-processing, including data cleansing, annotation and transformation. Then the team defines ML solution evaluation criteria and trains the model with supervised, unsupervised and reinforcement learning approaches. To achieve the desired output, our machine learning developers can build an ensemble of models while ensuring ML solution’s security and compliance.
Basic team composition:
- A data/machine learning engineer
- A project manager
- A business analyst
- A QA engineer
5
Integration & deployment
We identify a suitable deployment environment and create the strategy to integrate the ML solution into the business software. After we make sure the ML solution adheres to testing guidelines, we deploy it to the production environment, ensuring the ML model’s scalability, proper performance, and security.
Basic team composition:
- An MLOps
- A data/ML engineer
- A project manager
- A QA engineer
6
Ongoing support
We monitor the model's performance and improve the accuracy of ML output by retraining the solution using new data from the production environment with the customer’s approval, all without disrupting the solution’s operation. We provide user onboarding and training and, upon request, create an ML solution optimization strategy.
Basic team composition:
- A support engineer
- A project manager
Machine learning solutions across industries
Retail
- Product demand and retail trends prediction
- Targeted advertising, dynamic pricing, and promotions tailored to customers’ needs
- Anticipatory shipping and smart route planning
- Anomaly detection and intelligent video surveillance
Ecommerce
- Advanced recommendation and search engines
- Chatbots for improving customer experience
- Contextual shopping
- Demand forecasting
- Fraud and anomaly detection for safer purchasing experience
Healthcare
- Diagnostics and identification of high-risk patients
- Consulting chatbots and virtual assistants
- Drug discovery and development
- Automated EHR processes and virtual nursing
Banking
- Personalized service offerings
- Automated back-office operations and NLP capabilities
- Fraud detection
- Virtual assistants for customer support
- Customers’ credit profiles assessment
Education
- Tailored content recommendations and customized learning paths
- Adaptive training activities for personalized learning experiences
- Dynamic adjustment of learning speed and personal curricula
- Bots to automate and streamline manual processes
- NLP-based real-time translation and transcription of eLearning content
Stock market
- Stock price prediction
- Trade execution and market making algorithms
- Stock market forecasting and autonomous stock tracking
- Automated fraud detection and customizable alert systems for managing risk
Marketing
- Automated customer segmentation and new customer segments discovery
- Ad personalization and contextual advertising
- Marketing automation
- NLP and generative AI capabilities for enhanced customer interactions
Real estate
- Personalized recommendations and virtual tours
- Market value prediction
- Automated property management
- Property performance monitoring and price prediction
Manufacturing
- Predictive maintenance to maximize asset lifetime
- RPA and digital twins for production efficiency improvement
- Augmented quality control to timely identify anomalies
- Product demand forecasting
Logistics
- Dynamic supply and demand balancing for a resilient supply chain
- Estimated time of arrival (ETA) and warehouse workloads prediction
- Dynamic route optimization for timely delivery of goods
Agriculture
- Automated disease detection and treatment recommendations
- Yield mapping and estimation
- Crop quality detection and classification
- Livestock health aspects monitoring
Hire machine learning engineers from Itransition
Machine learning solutions for real-world use cases
Computer vision
We build solutions that enable computers to process, analyze, and interpret visual data, such as images and videos, to trigger an action or a set of actions.
Customer tracking
Itransition’s team creates solutions with functionality for processing video streams in real time to detect and count humans, analyze customer activity within a store, and identify suspicious behavior.
Quality control
We enable AI-based defect detection in manufacturing and implement automated solutions for visual quality control of production processes and assembly lines to streamline error-prone manual inspection processes, increase speed and accuracy, and facilitate objectiveness and scalability.
We create solutions that analyze X-rays, computed tomography scans, ultrasound images, mammograms, and magnetic resonance imaging data to identify abnormalities not visible to the human eye, make a diagnosis, rate disease progression and treatment response.
Natural language processing
We build ML-powered solutions to interpret and extract meaning from written and oral natural language.
We help businesses streamline customer services with chatbots that assist customers and answer their questions 24/7, making customer journeys smoother and freeing up support agents’ time.
Sentiment analysis
We develop AI systems to help organizations get valuable insights from voluminous customer data and use them to optimize their products or services and improve customer experience.
TTS/STT conversion
We create ML-enabled solutions for automatic speech recognition and conversion into text and vice versa to support virtual assistants’ and chatbots’ operation, voice input and commands, call center transcription, medical record analysis, and voice translation.
Data mining
Our ML experts create solutions using deep neural networks such as artificial neural networks and recurrent neural networks that are able to aggregate voluminous data sets to derive business value out of them and solve non-trivial problems.
Itransition’s machine learning experts deliver custom recommendation systems that automatically process data output generated by customers to segment them based on the pre-defined criteria and target them with personalized content, including product recommendations and personalized offers.
Itransition develops ML-based solutions that are trained to predict potential dangers, such as fraudulent transactions, machine breakdown, or patient condition deterioration, helping organizations prevent them.
Our team delivers ML-powered software that timely detects suspicious behavioral patterns indicative of fraud like money laundering, market manipulation, and tax or insurance fraud.
Predictive maintenance
Our experts deliver AI services for predictive maintenance in manufacturing to reduce equipment downtime and expenses, increase asset lifetime, and improve overall operational efficiency.
Related services
Artificial intelligence
We deliver AI applications to help businesses drive automation across the enterprise, improve product and service quality, enhance customer experience, and solve non-trivial business problems.
Predictive analytics
Our team develops solutions powered by predictive models for identifying market trends, forecasting risks, predicting customer behavior and product demand, and supporting data-driven decision-making.
AI agents
We build AI agents that help businesses automate repetitive, time-consuming tasks, enable access to data insights across teams, serve customers 24/7, and strengthen corporate cybersecurity.
Conversational AI
We develop AI tools able to conduct human-like conversations for engaging target audiences with personalized offers, assisting customers across channels, and providing insights to employees based on natural-language queries.
Generative AI
Our team delivers AI solutions with capabilities to summarize, analyze, and generate various types of content, helping businesses streamline content creation tasks, facilitate communication with customers, and enable self-service data analytics.
Bring your ML solution to life with Itransition’s experts
Machine learning technologies we use
- TensorFlow
- Scikit-Learn
- NumPy
- SparkML
- DarkNet
- XGBoost
- Faiss
- Keras
- NLTK
- Sonnet
- Catboost
- Annoy
- PyTorch
- Pandas
- Residual neural network (ResNet)
- Categorization models
- YoloNet
- RetinaFace
- Recurrent neural network (RNN)
- Generative adversarial network (GAN)
- AlphaPose
- U-Net
- Convolutional neural network (CNN)
- Large language models (LLMs)
- Neural radiance field (NeRF)
- Skeleton detection
- DBSCAN
- Regression models
- Clustering algorithms
- Pose2Seg
- Amazon SageMaker
- Amazon Rekognition
- Amazon Lex
- Amazon Polly
- Azure Machine Learning
- Azure Cognitive Services
- Azure Bot Services
- Vertex AI
- Google Cloud Vision API
- Google Cloud Natural
- Language AI
- Google Cloud Speech API
- DialogFlow
FAQs
What can machine learning do for my business?
ML implementation brings numerous benefits, the most significant being cost reduction due to the automation of repetitive manual tasks and an increase in operational efficiency due to augmenting human intelligence.
How do we start with machine learning?
Every machine learning project begins with a careful analysis of the company’s business needs, selecting the most suitable solution and technology stack, and, when needed, delivering a proof of concept (PoC) for validation of the approach and demonstrating the solution’s value.
How much does a machine learning solution cost?
To define the TCO of an ML solution, you should factor in:
- Data-related attributes (number of data sources, data type, data volume, and data quality used for ML development)
- ML accuracy requirements
- ML approach and methodology
- ML implementation and maintenance costs
- Infrastructure costs
- Software licensing
Contact our ML consultants to get a ballpark estimate for your project.
What engagement models do you offer?
Whether you need skilled ML experts for a short-term ML project or a cross-functional team for a longer, full-time engagement, we offer flexible cooperation options to address your specific needs. We offer full-project outsourcing for a turnkey solution delivery, dedicated teams for increasing the expertise and development capacity on your project, and staff augmentation for closing skill gaps in your team with pre-vetted professionals.
What are the major challenges of ML model implementation?
Major challenges during ML implementation span poor data quality (e.g., incomplete datasets), model limitations (e.g., over- or underfiiting), deployment barriers (e.g., integration complexity). Addressing these obstacles requires technical expertise and adherence to fundamental data management and MLOps practices. Consider turning to trustworthy ML experts with proven expertise in artificial intelligence, deep learning, big data, data science, and data engineering to ensure smooth implementation of a high-quality, cost-effective ML solution.
Does training an ML model require additional infrastructure costs?
We handle ML model training on our infrastructure, so you’ll only need resources for deploying the solution after development.
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