ai021 · NanoSI: Structured Intelligence

Engineering intelligence you can verify.

NanoSI organises what an organisation knows into a verified, four-dimensional lattice, the tesseract. Small AI models then reason over it, so every answer can be traced back to a source.

Train Your Own LLM companion page

Wireframe of a tesseract: an outer cube and an inner cube, joined vertex to vertex.
A tesseract: a cube extended into a fourth dimension. What you see is its shadow turning in 3D.

Mission

ai021 builds AI you can verify. Our small models answer from organized, source-linked knowledge, so every answer can be traced back and checked.

Vision

A world where trusting AI means checking it, because every fact it uses has an address you can look up.

Scale doesn't buy trust

Bigger is costlier.

Running very large general models for narrow, repeated work is expensive. Small models trained for one job can match larger ones on that job (sources on Technology).

Black boxes can't be audited.

If a model can't show where an answer came from, compliance review becomes guesswork.

The tesseract idea

Why a tesseract?

A square locates things in two directions. A cube adds a third. A tesseract adds a fourth. You can’t see it directly, but you can see its shadow, the rotating shape above.

Most AI keeps what it “knows” blurred inside billions of numbers. You can’t point to where a fact lives or where it came from. NanoSI does the opposite. It places every fact at a precise point in a four-dimensional lattice. One direction says what it’s about, one says how it connects, one says where it came from, and one says when it was true. Because each fact has an address, every answer can be walked back, step by step, to its source.

  • Traceable: every answer has a path back to a source.
  • Small: the model reasons over the lattice; it doesn't have to memorise it.

The tesseract, explained

How NanoSI works

1

Miner

Extract facts from documents, keeping their source.

2

Hyper-Lattice

Place each fact in the verified, four-dimensional lattice.

3

Curriculum

Generate lessons by walking the lattice, simple to complex.

4

Fusion Model

Train a compact model to reason over it and cite it.

5

Evaluation

Check answers against the lattice; export an audit trail.

Provenance is kept at every stage, from document to fact to answer.

The Foundry, stage by stage

How we measure

We write the test before we build the model. Every engagement starts with a private evaluation set and written success and stop criteria.

Dr. Zafrullah Khan, Founder & CEO, ai021

Author of Train Your Own LLM (coming soon)

“I teach how today's LLMs work. NanoSI is what I'm building next.”

Get in touch

Cover of Train Your Own LLM by Zafrullah Khan, Ed.D.

Train Your Own LLM

20 chapters in five parts, about 800 pages, on how language models work, and how to fine-tune and build one.

Coming soon

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