Generative AI

Make something from (almost) nothing.
Just add data.
You can't take a step these days without hearing about Generative AI, aka GenAI. GenAI is best known for creating an endless variety of new content, from text to images, based on patterns it learns from existing data.
At its most basic, GenAi is ideal for businesses needing to scale content creation without sacrificing quality. It helps generate everything from marketing copy to new product designs quickly and efficiently.
That's not all. Advanced generative models and GenAI systems can also have excellent reasoning skills, enabling some unexpected analytic and recommendation capabilities.

Knowledge Management and Extraction

AI helps organize, manage, and retrieve knowledge from vast datasets, making it easily accessible, enabling companies to leverage their collective knowledge effectively and ensuring valuable information is never lost and that you can get the most out of it.

Personalized Content Generation

GenAI makes it possible to tailor content to individual users’ preferences and behaviors, from personalized emails to customized product recommendations. Personalization enhances user engagement and satisfaction, driving better marketing results and customer loyalty. This applies to both “text-to-text”, in which GenAI translates or transforms text input into desired text output, such as summarizing long documents or translating languages, and “text-to-image”, in which GenAI converts written descriptions into corresponding images.

Synthetic Data Generation

Very often machine learning projects find themselves in a situation in which they don't have enough real data on which to train a model, or the real data they have is too sensitive or biased to use. GenAI lets you create artificial data you can use for training machine learning models without compromising that real data.

Customer Support Automation

Customer inquiries can be routed through AI-powered chatbots and support systems, enhancing customer service by providing quick, consistent, and accurate responses. Thus, wait times can be reduced and human agents freed up for more complex issues.
Data Gathering
Gather the data you'll use for your experiments
Data Experimentation
Analyze data using different components and hyperparameters
Data Engineering
Ensure experiments are running properly in development and production
Data Science
Extract useful insights and suggest further experimentation
Traditional ML
Analyzing business and other data
Ensuring AI/ML operations run smoothly
Generative AI (LLMs)
Create content or generate answers based on existing data
Ensuring AI/ML operations run smoothly
Deploy, maintain, and observe existing generative AI models
Custom Models
Create custom models that best suit your use case
Repeat the process as many times as necessary