Custom generative AI development services

The cost of developing generative AI solutions depends on various factors, including team composition, data quality, project scope, required compute power, cost of infrastructure, integration, and maintenance. It’s challenging to estimate the cost without knowing the details of your project. Simple generative AI PoC (proof of concept) projects cost $20-50K, while a full implementation can cost anywhere from $50K to even over $500K. Accurately creating realistic estimates requires evaluating needs, requirements, and possibilities to assess the complexity and workload of your project.

Assessing the time needed for AI product development requires considering various aspects of the project—such as its complexity, required development team size, resources, training data, and expertise, to name a few. Simple projects can last weeks to months, while more complex ones can take years of research and development. If you aren’t sure how to utilize the potential of AI, you may also need generative AI consulting services which also add a few days to your project timeline.

Each software development company may offer different services to deliver high-quality generative AI solutions. Typically, such services include AI consulting services to design AI solutions tailored perfectly to your needs, product development, seamless integration of generative AI solutions into existing systems, and maintenance services to ensure everything works smoothly. To unleash the full potential of generative AI, generative AI companies often offer complimentary services of data collection and/or audit, software development, and more.

It’s easy! Fill out our contact form, and we’ll invite you for a non-binding call to discuss your business requirements. During the call, we’ll talk about the feasibility of using advanced AI models for the certain use case, discuss the potential risks of such a project, and walk you through the potential next steps that will take place if you decide to work with us.

In short, generative AI creates new content, while analytical artificial intelligence analyzes existing data for insights and predictions. Although these are two different types of AI systems, they have much in common (e.g., both require an R&D approach), so experience in analytical AI has great value for generative AI software development.

Proficiency in high-risk and R&D projects and focusing on quick validation of business hypotheses (you don’t want to waste years on testing something that could be validated within weeks or months) is what you’re looking for. And that’s why you’re in the right place! Building AI solutions since 2017, we worked out an approach to generative AI development that allows us to test your business hypotheses as fast as possible so you get the best chances for your projects’ success.

Read also: How to Start With Artificial Intelligence: Your First Steps With AI

There’s no doubt generative AI can create many exciting opportunities. But let’s not forget it also comes with quite a few risks and challenges! That’s why, when thinking of utilizing AI technology, may it be GenAI development, machine learning, natural language processing, or anything else, it’s crucial to consider those hurdles and be ready to address them.

What kind of risks and challenges do we mean? E.g., data security, compliance, IP rights, biases, errors, and limitations of the models, to name a few.

You have just found one! Read our customer reviews on Clutch, see our past projects, and contact us to learn more about custom generative AI development services.

Our stack of generative AI tools include: GPT-3.5 Turbo, GPT-4, Pinecone, Langchain, Haystack, AWS, Azure, Modal, and Cognitive Search. The list is constantly growing as we adopt new technologies. Also, keep in mind that we always adjust the tech stack to specific project needs.

AI-generated content can be utilized in various areas – in AI chatbots, intelligent AI agents, writing assistants, and so on. For the one that you described, it will be useful to use a foundation model (e.g., GPT-4) and RAG architecture. Thanks to that, we will be able to reduce the model’s hallucinations and make sure that the answers produced by the model are accurate.

Read more: RAG vs. Fine-Tuning vs. Prompting. How to Reduce Generative AI’s Hallucinations and Boost Its Performance?