AI consulting services

Engage AI to level up process automation, quickly react to market changes, improve customer service, detect and solve business problems, gain a lasting advantage over competitors, and more.

  • Optimize labor costs with AI-infused workforce optimization systems that track performance and provide actionable insights

  • Handle multi-dimensional issues and predictions with machine learning approaches that automate evaluation processes

  • Speed up security risk discovery, pattern analysis in your datasets, and cyber threat prevention with integrated machine learning

  • Expand new business models to monetize AI and reimagine value creation using machine learning, computer vision, and natural language processing

  • Increase profits with AI-augmented customer support, personalized user experiences, and customer analytics insights that help drive sales

  • Find and retain the best talents by sourcing, screening, and hiring top candidates, onboarding them, and determining equitable pay

Yalantis’ artificial intelligence consulting services

  • 01

    Data strategy and architecture

    Assess your company’s data infrastructure, data quality, and data management practices to check if you have the necessary foundation for AI adoption. Receive recommendations on data collection, storage, and governance processes to prepare for AI implementation.

  • 02

    AI implementation roadmapping

    Define your AI strategy and develop a roadmap to integrate AI into your operations. This service includes defining your business goals, assessing your capabilities, identifying areas where AI can add value, and outlining the necessary steps to develop an AI-based project.

  • 03

    AI/ML solution feasibility study

    Evaluate the viability and potential benefits of implementing AI and ML technologies with a feasibility study. This entails a comprehensive assessment of your business requirements, existing infrastructure, and available resources.

  • 04

    AI solution design and development

    Design and develop AI solutions tailored to your specific business needs. This typically involves building and deploying custom AI models, leveraging prebuilt AI platforms, or integrating third-party AI technologies.

  • 05

    ML-powered module creation

    Benefit from custom software modules that utilize machine learning output and their integration with an AI solution by making better decisions, enhancing efficiency, delivering personalized experiences, gaining a competitive advantage, and driving continuous improvement.

ML- and AI-powered solutions Yalantis builds

  • AI-driven forecasting

    Collect and process a wide range of data including historical data, customer demand data, market data, currency data, and news content. Build predictions with supervised ML, unsupervised ML, autoregression models, and more.

  • AI content analysis

    Benefit from natural language processing, optical character recognition, text preprocessing and evaluation, spam filters, document clustering, and linguistic research. Turn images, videos, and audio content into data sources and get trend labels as output for further predictions.

  • Portfolio optimization

    Build fast and self-improving algorithms to solve the combinatorial optimization problem. Solve other real-life optimization problems with advanced tools such as genetic algorithms, simulated annealing, stochastic local search, and reinforcement learning agents.

  • Customer segmentation

    Use all your data to create advanced customer segments and cohorts. Use customer segments for building segment-specific forecasts, discovering customer behavior trends, planning targeted marketing, and launching new features.

  • Fraud detection

    Implement an AI-based anti-fraud
    system strengthened by ML. Discover and prevent email phishing, credit card and payment fraud, identity theft, document forgery, form jacking, account takeovers, and more.

  • Data mining

    Build a data warehouse to structure your data for analytics and decision-making. Use diverse techniques for data mining including association analysis, regression analysis, and classification and predictions.

Need a solution or service that’s not listed above?

Contact us and learn what we can offer to meet your ML and AI app development needs.

Request more information

Machine learning tech stack

  • Matplotlib

  • Hadoop HDFS

  • Tez

  • TensorFlow

  • Keras

  • NumPy

  • Apache Airflow

  • Google Cloud

  • Seaborn

  • SciPy

  • ELK Stack


  • Caffe

  • Python

  • Jupiter

  • PostgreSQL

  • Sqoop

  • Hive

  • Django

  • Scikit-learn

  • Hadoop Hue

  • Plotly

  • Flask

  • Apache Spark MLlib

  • R

  • SQLAlchemy

  • Theano

  • Amazon Web Services

  • Microsoft Azure

  • Pandas

Looking for different solutions or technologies?

Let us know and we’ll see what we can offer.

Request more information
Frequently asked questions (FAQ)

Why is Yalantis a trustworthy artificial intelligence consulting company?

Yalantis has been named a leading AI development provider by C2Creview, a research and IT company review platform. Our AI consulting company has an overwhelming 90%+ customer satisfaction score. Fortune’s top 10 organizations are among our clients, and we are one of the top 10 software development providers according to Clutch. We consult and develop software using AI, data science, as well as business intelligence (BI) and analytics. Our artificial intelligence consultants are experienced in providing compliance with domain-specific requirements (HIPAA, GDPR, PCI DSS, etc.) to create compliant AI solutions.  

What are common use cases of AI/ML across business domains?

  1. Fraud detection and risk management. In industries such as finance, insurance, and e-commerce, AI and ML can help identify fraudulent activities, detect anomalies, and assess risks. These technologies enable businesses to mitigate risks, prevent financial losses, and enhance security measures.
  2. Predictive maintenance and anomaly detection. AI and ML can be used for predictive maintenance in industries including manufacturing, transportation, and energy. By analyzing sensor data and historical maintenance records, businesses can predict equipment failures, schedule proactive maintenance, and minimize costly downtime.
  3. Natural language processing (NLP) and sentiment analysis. NLP and sentiment analysis technologies are helpful for analyzing customer feedback, monitoring social media, and conducting market research. These technologies can analyze textual data to understand customer sentiment, identify emerging trends, and gather actionable insights to drive business decisions.
  4. Computer vision. Expertise in computer vision allows businesses to leverage AI and ML for visual recognition, image analysis, and object detection tasks. Computer vision is applicable in healthcare, retail, manufacturing, and security, enabling businesses to automate processes, enhance quality control, and improve user experiences.

Does your help in streamlining business efficiency go beyond AI consultancy and development services?

Besides providing AI-related services, we offer other services to help you improve your business processes and workflows: 


Data science solutions:

  • Data engineering 
  • Data modeling
  • Data integration
  • Data restructuring
  • Data migration and maintenance
  • IoT data management


Big data, BI, and analytics:

  • Time series data analysis and forecasting
  • Decision-making support 
  • Data structuring and visualization
  • Service personalization


Robotic process automation:

  • Intelligent document processing
  • Automated front-office support
  • Automated security records
  • Improved recommendation systems

What industries can benefit from Yalantis’ artificial intelligence consultancy and development services?

AI- and ML-based software solutions can significantly increase value and efficiency for businesses in a range of industries. Below are just some of the possible industries and benefits:



  • Optimize payment processes
  • Interpret medical images while minimizing errors
  • Create drug discovery solutions
  • Use an AI system that automatically learns for patient engagement 



  • Automate fraud detection and risk exposure analysis
  • Safely manage customer data
  • Build AI solutions for financial advisory
  • Use virtual assistants for customer support



  • Improve service personalization and product recommendations
  • Apply predictive analytics for demand forecasting 
  • Streamline visibility at all supply chain stages
  • Implement self-service assistants for customer support



  • Use predictive maintenance 
  • Build solutions for remote management and autonomous control
  • Monitor manufacturing conditions and detect anomalies
  • Use neural networks for manufacturing simulations

Yalantis’ approach to AI consultancy and development

In the rapidly evolving world of AI, our company stands at the forefront of cutting-edge development and artificial intelligence consultancy, harnessing the power of data to create intelligent solutions. Our approach to AI development is comprehensive, encompassing various steps to ensure the delivery of accurate predictions and valuable insights:

  1. Define the problem. At the outset, we work closely with our clients to precisely define the problem they aim to solve or the insights they seek to gain through AI. This involves identifying relevant data sources and understanding the client’s business objectives, identifying pain points, and establishing clear AI project goals.
  2. Gather data. Our experts collaborate with clients to identify and gather relevant data from diverse sources into a single system, such as a data lake. This can include structured and unstructured data from customer records, sensors, social media feeds, and other sources.
  3. Data discovery and profiling. Data discovery involves exploring and understanding the collected data, identifying its characteristics, and gaining insights into its structure, quality, and potential limitations. Data profiling helps us understand its relevance, completeness, and potential biases.
  4. Data cleansing and analysis. Data quality is paramount for effective AI development. We employ advanced techniques to clean and preprocess data, removing inaccuracies, inconsistencies, duplicates, and outliers. This helps us ensure the reliability and integrity of data used for training and modeling. We identify and correct errors, fill in missing values, transform variables, and perform visualization and exploratory analysis techniques to find patterns.
  5. Data structuring. To facilitate efficient analysis and modeling, we structure data to optimize its accessibility and usability. This includes organizing it into logical and consistent formats, such as tables, databases, or other suitable structures.
  6. Data preprocessing. Now it’s time to turn the data into a format applicable to AI algorithms. We may employ techniques like feature engineering, dimensionality reduction, or normalization to enhance the data’s presentation and highlight meaningful patterns or relationships. Additionally, we enrich the data by integrating external sources or incorporating domain-specific knowledge.
  7. Data validation and publishing. To ensure the accuracy and reliability of prepared data, we subject it to rigorous validation processes. This involves cross-checking it against ground truth or known outcomes and validating against predefined quality criteria. After validation, we load the structured data to a data warehouse in a secure and accessible format for further analysis and modeling.
  8. Feasibility study. Prior to developing a model, we evaluate the feasibility of the project, dig into the data, and run experiments to establish baseline performance, including assessing the accuracy of potential models. Then we select the most appropriate model and estimate the resources and effort required for development.
  9. Develop and train machine learning models. Using state-of-the-art algorithms and methodologies, we develop and train the chosen AI model using prepared data. This involves configuring the model’s parameters, optimizing performance, and iteratively refining it to achieve the desired accuracy and predictive capabilities.
  10. Fine-tune the model. After initial training, we fine-tune the model by iteratively adjusting its parameters, conducting performance evaluations, and incorporating feedback from domain experts. To fine-tune the model, we use such techniques as cross-validation and grid search. This enhances the model’s accuracy, robustness, and generalizability.
  11. Integrate the model. Once the model achieves satisfactory performance, we seamlessly integrate it into the client’s existing infrastructure or application ecosystem. This ensures that the AI solution can efficiently process new data and deliver real-time predictions or insights.
  12. Refine the model. We test the accuracy and performance of the model on new data and validate its effectiveness in predicting or giving insights on target parameters. We refine the model as needed based on user feedback and evaluation. As AI development is an ongoing process, we continually refine and optimize the model over time. We monitor its performance, gather feedback, and proactively update the model to adapt to changing data patterns or evolving business needs.
  13. Deliver predictions in the form of reports. Finally, we provide predictions and insights derived from the trained model in the form of comprehensive reports tailored to meet the client’s requirements. These reports offer actionable recommendations, visualize key findings, and empower informed decision-making.

Our company’s approach to AI development combines meticulous data processing, advanced modeling techniques, and iterative refinement to deliver powerful solutions and actionable insights. By adhering to the essential steps above, we ensure the accuracy, reliability, and value of our AI solutions, driving innovation and enabling our clients to stay ahead in an increasingly data-driven world.

Tips on how to choose an artificial intelligence consulting provider

As demand for AI continues to surge, choosing the right artificial intelligence consulting company becomes crucial for businesses aiming to effectively leverage the power of AI. There are lots of artificial intelligence consultants on the market, so it’s important to carefully evaluate them and choose one that meets your particular needs and goals. Below are the key factors to consider when choosing an artificial intelligence consulting service:

Expertise and experience. Begin by assessing the expertise and experience of the AI consulting company. Give preference to AI consulting services providers that have demonstrated success in engineering, building, and deploying AI models. Evaluate their portfolio of successful projects, industry experience, and technical proficiency in relevant domains. An experienced provider is more likely to understand the complexities of your business and offer tailored AI solutions.

  1. Collaborative approach. An effective AI consulting provider should adopt a collaborative approach. This presumes active engagement with your team, close attention to your requirements, and a grasp of your business goals. Look for a provider that values communication, transparency, and ongoing collaboration throughout the project lifecycle. A consultative partner will work closely with you to co-create solutions that align with your vision.
  2. Customization and scalability. Consider whether the AI consulting provider offers customized solutions that fit your unique needs. AI projects should be tailored to address specific business challenges and opportunities. Additionally, scalability is crucial, as your AI initiatives may evolve over time. Ensure the provider can accommodate future growth, adapt to changing requirements, and scale AI solutions seamlessly.
  3. Data privacy and security. Artificial intelligence consulting services involve handling sensitive data, making data privacy and security paramount. Make sure that your future AI technology provider takes strict data protection measures and follows industry best practices. They should have a comprehensive understanding of data privacy requirements and be able to implement appropriate safeguards to protect your data throughout the AI development process.
  4. Ethical considerations. AI technologies raise ethical considerations and have societal impacts. Reputable AI consulting firms should be knowledgeable about principles and guidelines of AI ethics. They should prioritize fairness, transparency, and accountability in their AI solutions. Inquire about a company’s approach to mitigating bias, ensuring ethical data usage, and addressing potential societal implications.
  5. Proof of concept and pilot projects. Consider engaging in a proof of concept (POC) or pilot project before committing to a long-term partnership. This allows you to evaluate a provider’s capabilities firsthand and assess the feasibility and potential value of proposed solutions.
  6. Domain knowledge. Consider a provider’s knowledge and understanding of your industry or specific domain. AI solutions are highly context-dependent, and a consultant well-versed in your industry will have insights into domain-specific challenges, regulations, and trends. This ensures that created AI solutions are not only effective but meet domain-specific requirements.
  7. References and client reviews. Request references or check out reviews from a provider’s existing clients. Get in touch with these references to collect and evaluate feedback on their experience, project results, and overall satisfaction. This firsthand information can help you assess the provider’s reputation, reliability, and ability to meet expectations.


Providing our own AI consultancy services, we know that picking the most suitable AI consulting firm is a crucial decision that will influence the success of your AI implementation initiatives. By considering factors such as expertise, domain knowledge, collaboration, customization, data privacy, ethics, and references, you can make an informed choice. Trusted and capable AI consulting companies not only guide their clients in implementing AI solutions but become long-term strategic partners, enabling organizations to unlock the full potential of AI.