CAROUSEL CONNECT

AboUT CERN

CERN represents a new model for building AI: one where subject matter experts lead and engineers support. Created as an alternative to extractive LLM training pipelines, CERN centers expertise, authorship, and governance in the development of intelligent systems.

The Issue

Traditional AI training treats experts as temporary labor. Their knowledge is harvested, flattened, and repackaged into systems they do not control.

The Solution

CERN flips this hierarchy. Here, experts are not add-ons. They are the architects, decision-makers, and stewards of the models that represent their fields.

The Reward

CERN is where collaboration replaces extraction. Where expertise is valued, compensated, and centered. Where the people who know the work finally lead the systems built to represent their knowledge and expertise.

Why CERN Exists

To Fix What’s Broken

Most AI systems today are trained through pipelines optimized for scale, not fidelity. Experts are brought in late, undercompensated, and given no authority over how their knowledge is used. Their insights are broken into fragments, stripped of context, and absorbed into models that cannot explain their reasoning or trace their lineage. This produces systems that are broad but shallow -- powerful but unreliable.

To Build Something New

CERN exists to build something different: AI with roots, authorship, and governance. We believe expertise is not a dataset to be extracted, but a relationship to be cultivated. And we believe the people who understand a domain should define how it is represented in intelligent systems.

To Define A Better Path

CERN imagines a future where AI is not merely a mirror of the internet, but a reflection of real expertise. A future where knowledge is stewarded, not scraped. A future where experts lead the systems built to represent their work. We are building the world's first ecosystem of expert-authored, micro-domain AI models -- precise, trustworthy, and governed by the people who know the work best.

1. Experts Define the Domain

CERN begins where all trustworthy intelligence should begin: with the people who know the work.

Experts establish the boundaries, standards, logic, and failure modes of their field.
They determine what "good" looks like -- and what must never be compromised.
Their insight is treated as architecture, not annotation.

This is the opposite of extractive pipelines, where experts are brought in late and their expertise stripped of context.

2. Engineers Translate Expertise

Engineers support experts by transforming domain knowledge into structured, high-fidelity systems.

Decision trees, protocols, and model architectures are built from expert logic.
Context is preserved rather than flattened.
Every transformation is transparent and traceable.

This is what optimal training requires: translation, not extraction.

3. Models are Co-Authored

CERN models are not built for experts -- they are built with them.

Experts review, refine, and approve every layer of the model.
Versioning is transparent.
No update happens without expert oversight.
Every model is built with expert oversight.

This ensures the model reflects real-world standards, not probabilistic approximations.

4. Governance is Continuous

Intelligence is not static. Neither is expertise.

Experts retain authority over updates, corrections, and expansions.
Models evolve with the domain -- not with random internet data.
Governance is a living process, not a one-time handoff.

This is the foundation of trustworthy AI: stewardship, not set it and forget it.

5. Provenance is Preserved

Every decision has lineage. Every update has a steward. Every model has an author.

No black boxes.
No mystery data.
No invisible labor.

This is what ethical, expert-authored AI requires: accountability at every layer.

6. Intentional Alignment

CERN's workflow is intentionally aligned with what AI training should be:

Experts are partners, lot labor.
Models are built around coherent, expert-defined domains.
Every rule, source, and decision is traceable.
Updates follow domain standards, not engineering convenience.
Models evolve within the field.
No scraping, harvesting, or repackaging of unpaid labor.

CERN is not just building better models -- it is modeling a better way to build.