Wireframe of a tesseract: an outer cube and an inner cube, joined vertex to vertex.
The wireframe is a tesseract, drawn as its shadow. Pause keeps the current frame.

The tesseract, explained

NanoSI is built on one idea: give every fact an address you can check. The tesseract is how we picture that address.

NanoSI³

NanoSI to the power of three

NanoSI³ is how we write the product. Three dimensions have to grow together: Structured, Security, and Super Intelligence.

NanoSI³ = Structured · Security · Super Intelligence

  • Structured

    Knowledge lives in an explicit lattice. Every fact has an address you can check, so an answer can be walked back to its source.

  • Security

    Capability without control is a liability. Audit trails, source limits, and human authority are designed to stay in the loop — so stronger models do not mean weaker oversight.

  • Super Intelligence

    The long research horizon: more capable systems whose power rests on structure you can inspect, not on a bigger black box. This is a research direction, not a near-term product claim.

The tesseract is how we picture the Structured dimension. NanoSI³ is the product that pairs that structure with Security and with the Super Intelligence horizon.

In 30 seconds

Why a tesseract?

Every fact has an address you can check. The tesseract is how we picture an address for every fact.

In 3 minutes

Why the shape rotates.

The animation shows its shadow in 3D. As it turns, the “inner” cube becomes the “outer” one. That is a good metaphor for the product: one body of knowledge, viewed from different angles. It’s the same facts, still connected.

Why it matters to a business.

  • Audit: any answer can be walked back to the lines of the documents behind it.
  • Honesty: if a fact isn’t in the lattice, the right answer is “not supported”, not a guess.

Why not just a bigger model?

NanoSI keeps facts where each one can be traced. An answer that is not supported stays unsupported.

Smaller, well-trained models can beat larger ones on a defined task. The sources we point to are Hoffmann et al., Training Compute-Optimal Large Language Models (Chinchilla) (external), Ouyang et al., InstructGPT (external), and the SmolLM3 model card (external).

Every answer links back to its sources.

Measurement and known limits

Known limits

  • Results depend on document quality, lattice completeness and domain.
  • The lattice can only verify what it contains.
  • No guaranteed performance level.

Pilots and deployment

Planned deployment options: cloud, your cloud, on-premise, air-gapped.

Discuss a pilot

Glossary

Structured Intelligence
AI whose knowledge sits in an explicit structure that can be inspected, so answers can be traced and checked.
NanoSI
Nano Structured Intelligence.
Tesseract
The tesseract is how we picture an address for every fact.
Provenance
Where a fact came from: document, section, page.
Verification loop
Every answer links back to its sources.
Audit trail
The record designed to link an answer to its facts and documents.
Hallucination
A fluent answer that isn’t supported by facts.
Grounding / retrieval
Giving a model real source text at the moment it answers.
Fine-tuning
Further training on focused examples. It mostly changes behavior, not knowledge.
Small language model
A model small enough to run privately or on-premise.
Token
A small piece of text. The model predicts one at a time.