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Empower your internal processes to grow your business faster than ever & be the first to introduce tools that the world was dreaming of.
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Not sure if your generative AI project idea is realistic? Worry not, we’ll help you find that out!
We’ll fine-tune the foundation models for domain-specific use cases and tasks to ensure your project’s success.
From validating ideas to launching your bespoke software, you can always rely on our expert team.
Generative AI projects are ever-growing & transforming, and we’re ready to help you with that.
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The cost of developing generative AI development solutions
depends on many 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 to develop generative AI-powered software requires considering various aspects of the project—such as its complexity, required 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.
It’s easy! Fill out our contact form, and we’ll invite you for a non-binding call to discuss your generative AI project idea. During the call, we’ll talk about the feasibility of using generative 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 artificial intelligence creates new content, while analytical artificial intelligence analyzes existing data for insights and predictions. Although these are two different types of artificial intelligence systems, they have much in common (e.g., both require an R&D approach), so experience in analytical AI projects has great value for developing generative AI projects.
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 projects for years, we worked out an approach 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 unlocks 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 Gen AI, 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, the matter of IP rights, biases, errors, and limitations of the models, to name a few. You can read all about it in the article on our blog: 5 Challenges of Generative AI Adoption.
You have just found one! Read our customer reviews on Clutch, see our past AI projects, and contact us to learn more about custom generative AI development services.
The technologies that we use the most often 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 use cases. 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
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