Human-centric Augmented Intelligence.

We built Nebuli in a way that mirrors the workings of the human brain.

Nebuli API-powered Plug-andPlay Augmented Intelligence.

Why human-centric augmented intelligence?

We created Nebuli to help our team and customers explore new, exciting and transformative technologies that empower people and communities around the world.

Nebuli’s unique model enables your team to easily apply personalised augmented intelligence framework to elevate your own smarter applications within your workflow or expand to wider markets faster and with a much higher level of data security.

  • No tedious integration work.

  • No replacement of your apps.

  • No lengthy staff training.

Like the human brain, Nebuli does not demand constant data input to generate intelligence.

Off-the-shelf AI systems rely heavily on large-scale data input (including junk data), combined with exhaustive machine-learning tools. This commonly used model may work well for large corporations and institutions with the resources to generate and manage massive amounts of data.

It’s not about AI vs humans. It’s humans plus AI. This is Augmented Intelligence.

Simon Jack – Co-founder & Chief Design Officer – Nebuli.

We focus on the quality and relevancy of the available data, not just quantity.

Most organisations do not own or generate enormous quantities of data to make legacy AI systems relevant or even functional.

Not to mention, the inevitable integration problems and countless staff training that come with off-the-shelf AI.

Nebuli works on the principle of generating maximum levels of long-term intelligence output from minimal input of usable information only.

Nebuli's augmented intelligence for teams of all sizes.

Unique human-centric ecosystem.

As an ecosystem, Nebuli infuses your existing workflow with powerful augmented intelligence services.

Our systems apply an omnichannel approach to dramatically reengineer the experiences that bring technology and people together in a more human-centric manner through the following steps:

  • Collect and manage your various datasets from different sources through a single channel.

  • Bring together the traditionally separate services of data security, compression, modelling, segmentation, classification and knowledge discovery into the same channel.

  • Combine several other layers of your integrated services such as your software, applications, UI/UX and data security protocols within your existing workflow.

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