GenAI Development

Want to do ‘something’ with GenAI, but not sure what exactly?

Inject GenAI into your workflows to make your business fast, responsive & agile.

Welcome to Codewave’s ‘GenAI Development’ services—where we find problems worth solving with GenAI such as – unresponsive customer support, lack of fresh imagination in content creation and manual report generation.

We inject GenAI into workflows that need simplification and automation like implementing conversational bots to engage your customers 24/7, making sense of Data and simplifying complex report generation.

Our capabilities of exploiting GenAI to make your business more efficient – gives your brand an AI MOAT.

Why you'll love us?

We’re 10x thinkers and change makers, driving extreme-value innovation through human-centric design and emerging tech, defying the traditional. With a track record of serving 300+ businesses globally, collaborating with VC firms, startups, SMEs, and governments, while also developing our own GenAI products – we’re obsessed with building high-impact products, ready for scale.

features

Why Codewave, for GenAI development?

What to expect

What to expect working with us.

We transform companies!

Codewave is an award-winning company that transforms businesses by generating ideas, building products, and accelerating growth.

Frequently asked questions

.01 /What is Generative AI?

Generative AI uses machine learning to create entirely new content, like text, images, or music, based on patterns learned from existing data.

.02 /What are some applications of Generative AI?

Creating realistic marketing copy or product descriptions.
Generating creative text formats like poems or code.
Developing new artistic styles or composing original music.

.03 /What are the benefits of Generative AI development?

Increased efficiency in content creation tasks.
Exploration of new creative possibilities and ideas.
Automating repetitive tasks and personalizing user experiences.

.04 /What are the challenges of Generative AI development?

Bias in training data can lead to biased outputs.
Ensuring the originality and quality of generated content.
Ethical considerations around deepfakes and potential misuse.

.05 /What are some key technical aspects of Generative AI?

Models: The core algorithms of generative AI are GANs, VAEs, and Transformers.
Data: Extensive and varied datasets are necessary for training models efficiently.
Loss Functions: Metrics like mean square error and cross-entropy quantify model performance during training.
Architectures: Special structures, such as the generator-discriminator setup of GANs or the encoder-decoder design of VAEs, define model behavior.
Hyperparameters: Parameters like learning rate and batch size require careful tuning for optimal performance.
Evaluation Metrics: Metrics like Inception Score and BLEU score assess the caliber of generated outputs.
Ethical Considerations: Problems like bias and misuse call for ethical frameworks in the development of generative AI.