The three pillars of AI

AI models are built on three pillars: algorithm, hardware, and data.
In recent years, companies like OpenAI, DeepSeek, Anthropic, and others have deployed state-of-the-art GenAI models. But it’s not just their algorithms or hardware that set them apart — it’s the data they feed into their models.

In other words: AI models are what they eat.

These large companies can hire dedicated teams to perform data curation, giving them access to incredibly large, high-quality datasets. But isn’t it unfair that only a small number of organizations have access to this powerful advantage?

We want to change that.
We want to enable pre-training and fine-tuning models for everyone — but how can we achieve this?

The history of data

In the early 2010s, models could only train on labeled data — also known as supervised training. Every document in a dataset had to be labeled by a human, making large datasets incredibly expensive to acquire.

Then in 2018, a breakthrough emerged: models could now be trained on unlabeled data — unsupervised training. This paradigm shift made large datasets far easier and cheaper to obtain.

However, a new dilemma arose: quantity does not equal quality.

Now that we have access to extremely large datasets with trillions of tokens, much of it is redundant, irrelevant, or low quality.

How can less data make AI models better?

Today, businesses are generating and storing more data than ever — but not all data is created equal.
From noisy, incomplete datasets to poorly labeled information, the wrong data can derail AI initiatives before they even begin.

By carefully selecting the highest-quality data points in a large dataset — and filtering out low-quality or redundant data — we can not only reduce dataset size, but also build more compact models with better performance.

Benefits include:

• Reduced training time
• Lower computational costs
• Lower energy consumption
• Improved model performance

Who are we

Kuben Labs was founded with a simple mission:
To make high-quality data curation and AI expertise accessible to everyone.

We believe great AI isn’t just about bigger models or more GPUs — it’s about feeding those models the right data and applying the right approach. That’s why we focus on two core areas:

Data Curation — Turning your data into your greatest asset

Most companies are sitting on a goldmine of data without realizing it. The problem?
It’s buried under noise, duplicates, and irrelevant information.

Our data curation process identifies, cleans, and organizes your most valuable data so your AI models can train on information that’s accurate, relevant, and impactful. The result: models that learn faster, perform better, and cost less to run.

Consulting — Finding the right AI solution for you

The AI field is evolving at lightning speed, with new architectures, techniques, and tools emerging almost daily. Choosing the wrong approach can waste months of effort.

We keep up with the latest research so you don’t have to. Whether you’re building a model from scratch, fine-tuning an existing one, or integrating AI into your workflows, we help you design a solution that’s both cutting-edge and practical for your needs.

Let’s get started

If you’re ready to unlock the real potential of your data, or want to make a chat about AI and data, contact us!