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Generative AI
What is generative AI?
Generative AI is a class of model that produces new content. Text, images, audio, code. It learns statistical patterns from a large body of training data, then generates outputs that follow those patterns when you give it a prompt.
Traditional machine learning mostly predicts or classifies. Is this email spam? Will this customer churn? It maps an input to a label or a number. Generative models work differently. A large language model predicts the next token over and over, and the result reads like coherent language. Diffusion models start from noise and refine it into an image. The output is something that did not exist in the training set, assembled from the structure the model absorbed. A support team drafting replies from a few bullet points, an LLM filling in the rest, is generative AI doing exactly what it does best.
The catch is that these models generate what looks plausible, not what is verified. They can state something false with full confidence. That gap is why production systems pair generation with grounding, retrieval, and human review rather than trusting raw output.
Generative AI at Dallonses
We build with generative AI where it earns its place, not because it photographs well in a deck. That usually means a real workflow with a real bottleneck. Drafting, summarizing, classifying, answering from a company's own documents. We start from the problem and work backward to the model, instead of the other way around.
Our artificial intelligence solutions ground the model in your data and keep a person in the loop where the cost of a wrong answer is high. We wire in evaluation so you can see whether outputs are actually getting better, and we design the fallback for when the model gets it wrong, because it will. Global brands bring us their hardest AI and machine learning challenges, and we ship systems that hold up once real users start poking at them.
Have a workflow that generative AI could actually move? Let's pressure-test it together.
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