Applications include content creation, design, gaming, and personalized recommendations in various industries.
Ethical concerns such as misinformation, copyright issues, and biases need to be carefully addressed.
Despite challenges, generative AI continues to push boundaries and opens up exciting possibilities.
Generative AI, also known as creative AI, uses algorithms to produce unique content autonomously.
Accelerate delivery. Pre-built generative-AI components let teams test, iterate, and ship up to 10× more use-cases compared with ground-up builds.
Stay flexible, not locked-in. GUI- and code-based tooling unifies any mix of LLMs, vector databases, or embedding models, so you can swap technologies as needs change—no vendor handcuffs.
Optimise cost and performance. Workloads can tap GPUs across multiple clouds, balancing latency, availability, and spend without extra integration work.
Experiment at lightning speed. Prompt-testing sandboxes and side-by-side comparisons surface the best model-parameter combo in hours, not weeks.
Build robust pipelines fast. Connect vector stores, retrievers, and prompts into a single endpoint and prototype advanced RAG systems by tweaking chunking or context strategies on the fly.
