Generative AI software development
Integrate custom generative AI models to assist you in service delivery by automating processes, offering ideas, or solving complex issues
Enhance existing AI solutions by synthesizing data for further training and improvement of your AI models
Use generative AI to provide customers with personalized content, services, and products based on their preferences and behavior
Fill in missing expertise at your company with a generative AI model to save on hiring expenses and ensure round-the-clock assistance
Value we have delivered to our clients
successfully delivered AI projects across industries
AI specialists and data scientists
average customer satisfaction rating
Up to 20%
increase in business productivity
Generative AI software development services Yalantis provides
Our engineers deliver well-balanced AI solutions by meticulously selecting generative AI models that align with your unique business needs. We ensure cost-efficient resource allocation for optimal AI solution implementation.
Generative AI solution consulting
Definition of business problems and requirements
Development of a custom AI strategy
Roadmapping for efficient AI implementation
AI model selection support: generative adversarial network (GAN), generative pre-trained transformer (GPT), large language model (LLM), variational autoencoder (VAE), stable diffusion
Generative AI model integration and deployment
Seamless integration of pretrained models into existing products
Thorough testing of integrated models
Implementation of security controls and measures
AI project documentation for efficient knowledge transfer
Onboarding of business users
Generative AI model support and maintenance
Post-release support of AI models
On-demand consultation on system improvements
Timely updates to maintain a sufficient performance level
Model fine-tuning to keep up with business needs
Continuous feedback to ensure the model’s quality
Tap into next-gen technology and stay ahead of the market
Integrate generative AI models to enhance your digital systems with content generation, problem-solving possibilities, and prompt suggestions on business process improvement.
Yalantis: A company with a proven portfolio
Automated banking security system
Implementing custom security controls
Ensuring a scalable security ecosystem
Integrating cost-efficient tools and technologies
Digital banking application enhancement
Redesigning the application to make it more convenient
Integrating an AI-enhanced chatbot for real-time support
Increasing user satisfaction while using the application
Energy consumption management system
Developing mobile and web applications
Enabling energy consumption forecasts
Integrating with FreshChat AI-powered live chat service
Perform a feasibility study to choose the best-fit AI model for your business
Research AI technologies and build a proof-of-concept AI solution to validate your ideas and define the potential value of AI for your business without a large upfront investment.
Insights of our generative AI development company
How to streamline your ML software development project
Learn whether ML is suitable for your business, common ML use cases, and key steps of the ML project lifecycle.
From AI history to practical implementation
Discover highlights of the AI and ML event Yalantis hosted this year.
Predicting house prices using a machine learning regression algorithm
Get step-by-step technical guidance on how to use an ML regression algorithm to efficiently predict prices in the housing market.
What differentiates generative AI software development from traditional AI solutions?
Generative AI solutions are different from traditional AI solutions in that they’re designed to generate new content including text, images, audio, and video. These solutions can, for instance, automatically create reports based on data collected over a particular time period. Generative AI models largely depend on the initial input (request, problem, or idea) you provide them with. The more accurate the input, the more accurate the output you’ll receive. Plus, generative AI’s ability to adapt and innovate based on input makes it a valuable tool for industries seeking to automate creative tasks and produce dynamic content.
Generative AI development can also include generation of synthetic data for training other AI models, such as data for training autonomous vehicles to learn how to navigate different hypothetical traffic situations. Generative AI models use neural networks to analyze your existing data and make it the basis for new original content.
Which types of generative AI models does your generative AI development company have the most experience with?
We have extensive experience in generative AI development and integration of diverse AI models such as the most popular generative adversarial networks (GANs) and the most recently discovered transformer models. Other models we specialize in integrating are diffusion models, generative pre-trained transformers (GPTs), large language models (LLMs), variational autoencoders (VAEs), and conditional variational autoencoders (CVAEs).
Your project requirements and business goals dictate the choice of model or combination of models to consider. We evaluate each model’s pros and cons with regard to your business specifics and figure out the optimal way to implement a model into your workflow without disrupting it. Next, we test performance to check whether the model functions as needed. It’s also possible to verify whether AI aligns well with your business needs by developing a proof-of-concept.
What are possible use cases of generative AI development in different industries?
The application of generative AI software development services varies across domains. For example, in healthcare, generative AI models can assist in protein engineering to facilitate drug discovery, synthesize medical images to train machine learning models or algorithms, and contribute to genomics research and medical coding.
Similarly, in the finance sector, generative AI plays a pivotal role in risk modeling and fraud detection by generating synthetic data for robust testing, enabling financial institutions to enhance security measures and effectively reduce potential risks. Financial companies can also benefit from AI by generating financial forecasts based on complex analysis of market trends.
In manufacturing, AI models can assist in designing new products. In the IoT industry, there are also many possibilities to apply generative AI models such as data augmentation to generate sufficient data elements and enable more complete and accurate analysis of IoT data.
How do you customize generative AI software development services to different business models?
To deliver tailored services, we first take into account your industry specifics, assess your business needs, and define your functional and non-functional requirements. All of that helps us form a team of professionals who not only have expertise in your domain but can proactively guide you through the entire model implementation process. Our dedicated team collaborates closely with your organization to ensure that the generative AI model aligns seamlessly with your unique goals and industry challenges.
We provide you with a customized feasibility study assessing the potential benefits of generative AI development for your business. Our team can also perform user surveys to ensure we choose generative AI tools that directly address your users’ pain points and help users simplify their daily routines. We create comprehensive documentation covering AI system configuration, usage guidelines, and troubleshooting resources tailored to your business model.
How do you ensure data safety during generative AI development?
Ensuring the security of your corporate data is the main prerequisite for building trust between you and our team during the generative AI development process. Among the robust security measures we follow are:
- implementing secure data infrastructure with a role-based access system
- ensuring encryption of your data at rest and in transit
- maintaining an optimal data lifecycle by applying data retention and disposal policies to store data only for as long as necessary and securely archive or delete it when it’s no longer needed
- enabling a privacy-by-design principle, which requires adhering to strict data privacy policies from the moment the project starts and throughout its execution
- designing an incident response plan to promptly deal with security vulnerabilities
Even though generative AI models can function as completely independent entities, we still enable manual validation of their output to avoid biases, ethical issues, or any disruption to your business reputation.
Yalantis’ approach to selecting and integrating generative AI models
Generative AI software development services have provided new perspectives on using such AI technologies as natural language processing and computer vision. As the AI field continues to evolve, organizations face the challenge of selecting and effectively integrating the right generative AI models into their workflows. Yalantis’ AI engineering team has their own approach to generative AI model selection and integration. Below, we offer insights into the decision-making process and best practices for seamless implementation of generative AI models.
Understanding generative AI models: insider’s view from a generative AI development company
Generative AI models are designed to generate content or data to solve a particular business problem, suggest ideas, automate routine manual work, and provide a new vision. These models have proven their versatility in various use cases, such as text generation, image synthesis, and data augmentation. From powering chatbots that respond much like humans to creating pieces of art or assisting in gene sequencing and helping genomic researchers facilitate drug discovery for serious diseases, generative models can be of great help as they continue evolving and producing more intelligent results.
How to select a suitable generative AI model
When choosing the appropriate generative AI model, our generative AI development team considers the following critical factors:
Business case. We deeply investigate your specific business case and project requirements before selecting the model. Different generative AI models are perfect for different tasks. For instance, GPT models are known for accurate natural language generation, while GANs are preferred for image generation and manipulation.
Data infrastructure. Our data engineering team assesses the type and amount of data you have or need to obtain for effective AI model training. Some models require large training datasets, while others can perform well with smaller amounts of data. For the generative AI software development process to run smoothly, we may need to update your data management practices (for example, integrate a data repository such as an enterprise data warehouse to seamlessly aggregate large datasets for simplified retrieval).
Computational resources. One of the common challenges of generative AI models is the fact that they can require massive computing power, especially during training. We need to ensure that your IT infrastructure can support the model’s performance requirements. And depending on your computational resources, our generative AI development company can select the appropriate generative AI model and ensure it operates as required.
Pretrained models. If your project and business requirements allow, we can consider implementing pretrained generative models when applicable, as this saves time and resources. Generative AI technology like GPT-3, for example, has already been trained on massive datasets. However, as a reliable generative AI development services provider, we make sure to consider potential security issues and make sure your business data is well-protected.
Evaluation metrics. We also help you with defining the model evaluation metrics and key performance indicators that align with your project’s goals to measure the value that integrating an AI model can provide for your business. Depending on your project specifics, you might focus on metrics such as bilingual evaluation understudy (BLEU) scores for language tasks or Fréchet inception distance (FID) scores for image generation.
Ethical considerations. When selecting an AI model, Yalantis’ generative AI development team takes into account such crucial factors as ethical concerns, biases, and potential misuse of generative AI models. We also implement safeguards to ensure the responsible use of AI models and can prepare tailored guidelines and documentation for your users to comply with. This way, we can help you avoid instances of data loss, misuse of generated data, and potential damage to your company.
Commonly used generative AI models at our generative AI development company
GPT models (e.g., GPT-3):
- Natural language generation. GPT models are especially useful for generating human-like text such as ads, product descriptions, social media posts, or any other marketing content. These generative AI models can create coherent and contextually relevant sentences, paragraphs, and even full expert articles.
- Natural language understanding. GPT models can also interpret and summarize text snippets, making them suitable for tasks like sentiment analysis and question-answering systems.
- Applications. GPT models are widely employed in chatbots to facilitate human-like conversations, to generate content for articles and blogs, and for language translation services.
- Image generation. VAEs are primarily used for creating new high-quality images that exhibit diverse content. These generative models are commonly used in tasks like generating artificial faces, creating artwork, or simulating data variations.
- Style transfer. VAEs can also be useful in changing the style of images, enabling tasks like converting photos into different artistic styles or applying filters.
- Data augmentation. Generative artificial intelligence models can expand datasets for machine learning models by creating synthetic data elements, helping to improve model generalization and performance.
Conditional variational autoencoders:
- Attribute-based generation. CVAEs extend VAEs by ensuring the generation of data based on specific conditions or attributes. For instance, they can generate images of different hairstyles or clothing styles based on user preferences.
- Image-to-image translation. CVAEs are useful in tasks such as converting satellite images to maps, turning sketches into realistic images, or changing day scenes into night scenes while preserving content quality.
Generative adversarial networks:
- Image generation. GANs can create high-resolution, realistic images that resemble real-life scenarios as well as 3D versions of objects. They are often used in generating human-like faces, animals, and natural scenery.
- Video creation and prediction. GANs can produce videos such as animations and special effects in the film industry. It’s also possible to extend videos with GANs by predicting the next sequences based on existing content. Generative AI’s ability to enhance the entertainment industry with lots of original content is tremendous.
- Style transfer. GANs can change the style of images or artwork, such as transforming a photograph into a painting in the style of a famous artist.
Recurrent neural networks:
- Text generation. RNNs are particularly suitable for generating sequential data. These models engage in deep AI research to perform tasks such as generating creative and captivating stories, composing music, and producing video captions, where context and sequence matter.
Transformer Models (e.g. BERT, T5):
- Natural language processing. Transformer-based models like BERT and T5 are useful in a wide array of natural language processing tasks, including article summarization, sentiment analysis, and language understanding. Transformer models provide fascinating results in various language-related challenges and applications.
Integrating generative AI models
Once we’ve made up our minds on the suitable generative AI model, as a generative AI development company, we can proceed to the iterative model integration process, which involves several key steps:
- Data preparation. If our task is to train a model with your unique data, then we first need to clean and preprocess that data to ensure it aligns with the model’s input requirements. Our generative AI software development services also include regular data quality checks to maintain the controlled model training flow. This step is crucial for achieving desirable results and efficiently training the model.
- Model training. After thoroughly preparing datasets, we can train the generative AI model on your data. Depending on the complexity of the model and the amount of data, this process may require a significant amount of time and computational resources. However, the results will be worth it, as the more data we use to train the model, the more accurate the output we can count on.
- Hyperparameter tuning. Once the complex training process is over, it’s important to fine-tune the model’s hyperparameters to optimize performance. Yalantis’ generative AI development specialists make sure to carefully perform fine-tuning so as not to disrupt the entire model integration process.
- Monitoring and validation. It’s also crucial to continuously monitor the model’s performance and validate the output it produces against ground truth data. Our team can implement an alert system to detect any issues such as low performance and immediately notify the support team about them. This way, we can ensure stable model functioning and solve issues as quickly as possible.
- Scaling opportunity. If you need, we can further scale your model to handle increased workloads. To make this happen, we can use distributed computing solutions and cloud services while maintaining a high level of data security and integrity.
- Deployment. The final step is actual integration of the model into your production environment. Our generative AI development team ensures the model can handle real-time requests and is robust enough to maintain consistent performance, accurately interpret inputs, and produce expected results.
- Testing framework. For each of our projects, we make sure to integrate an automated testing framework that continuously evaluates the model’s key parameters and delivers prompt reports for further evaluation and rollout of updates if necessary.
Challenges and considerations for successful AI model implementation
When providing custom generative AI software development services, we also address common challenges to ensure seamless generative AI work:
- Data privacy. We prioritize your data privacy and implement a wide range of security measures and controls that keep your data safe from unauthorized access, manipulation, or loss. Before even beginning generative AI development, we define the level of sensitivity of your data and think through all necessary solutions to keep it as secure as possible.
- Bias mitigation. AI models, including generative ones, are not immune to errors, especially when they are trained on diverse and potentially biased datasets. To avoid bias, we continuously monitor AI models after deployment. By employing advanced monitoring techniques, we can identify and mitigate bias in generative AI models, ensuring that they produce fair and unbiased results that align with ethical and social standards.
- Interpretability. Generative models tend to be rather complex, and it may be difficult to properly interpret the results they produce. Understanding why a model makes certain decisions is crucial for building user trust and accountability. To enhance model interpretability and help you validate a model’s trustworthiness, a generative AI development company follows various tried and true techniques such as attribution methods that provide meaningful insights into how the model makes its conclusions, shedding light on the decision-making process.
- Resource constraints. Smaller organizations may face computing resource constraints when implementing generative AI models. To bridge the gap between your company’s infrastructure capabilities and the massive workload you need an AI model to process, we often recommend implementing cloud-based solutions. Cloud computing platforms provide the flexibility and scalability required to accommodate resource-intensive AI workloads. This approach not only minimizes infrastructure costs during generative AI development but also allows organizations to access up-to-date hardware and software resources on demand.
Generative AI models are powerful tools that can drive innovation and efficiency across industries and help businesses reduce manual work, speed up service delivery, increase competitiveness, and tap into cutting-edge technology to remain modern with up-to-date services.
However, the successful implementation of generative AI models goes beyond selecting and integrating them with the help of a mature generative AI development company; it’s critical to get a comprehensive understanding of the challenges and considerations involved. By ensuring sufficient data privacy, mitigating bias, increasing output interpretability, and dealing with potential project constraints and risks, AI development companies can help organizations harness the full potential of generative AI while upholding ethical standards and achieving their objectives responsibly and effectively.
As the AI domain continues to evolve and new AI technologies continue to spring up, the challenges and considerations mentioned above will remain important for generative AI software development services. Diverse industries are eagerly embracing automation, machine learning algorithms, and data-driven decision-making, and the role of generative AI in shaping the future of business is becoming increasingly evident. Whether it’s in healthcare, FinTech, manufacturing, energy and resources, the creative arts, or beyond, generative AI’s ability to create, innovate, and optimize processes has the potential to drive groundbreaking advancements and fuel disruptive innovations.