An energy-based reasoning model that checks itself before it answers.
Today's models predict the next word, so they can be confidently wrong and can't say why. PRISM understands the concepts behind a question, checks its answer before it speaks and shows its reasoning.
One word at a time, or the whole answer first.
A chatbot commits to each word as it goes and is wrong by the end. PRISM checks the whole answer before a single word is written.
Three steps, before a single word.
Chatbots write first and never check. PRISM checks first and only then writes.
Concepts, not words.
PRISM learns concepts and how they relate inside its internal space, instead of learning which word usually comes next.
Energy measures fit.
Every candidate answer gets a score. Low energy: it fits everything the model knows. High energy: something in it contradicts.
Correct, then speak.
When the energy spikes, PRISM corrects the thought before any text is produced, and writes down why.
A contradiction costs energy.
A chatbot can say something false with total confidence, because nothing inside it measures whether the answer holds together. PRISM measures exactly that.
We call it a cognitive immune system: the moment a line of reasoning contradicts itself, the energy rises, and the correction happens before the answer reaches anyone.
More on energy-based modelsA model that watches its own thinking.
PRISM belongs to the family of energy-based models and joint embedding predictive architectures, the line of research Yann LeCun has championed as the way beyond language models. Those models reason over meaning instead of words.
PRISM adds what we needed for real decisions: it measures its own consistency while it reasons, and steers its own thoughts and choice of words from that measurement. How this differs from JEPA.
Architecture details are shared under NDA as part of technical due diligence.
Auditable AI: a record of every decision.
Because PRISM measures its own consistency, its reasoning can be observed while it happens. Every decision comes with a plain-language trace of how it was reached, generated by the architecture, not written afterwards.
GDPR gives people a right to meaningful information about automated decisions. From December 2027 the EU AI Act requires high-risk AI to keep logs and support explanations, and from December 2026 the Product Liability Directive covers software and AI. What an AI audit trail must contain.
Built for decisions that cannot be wrong.
Risk and compliance.
Decisions a regulator can question, with the reasoning attached.
Power, water, networks.
Automation that knows when it is contradicting itself, before it acts.
Sovereign and explainable.
Reasoning that runs in-country and can be reviewed by oversight bodies.
What investors see.
The proof of concept proves the mechanism, not fluent language at scale. This is what it shows today.
It follows direction.
You can guide how PRISM reasons, live, and watch it respond.
It contradicts illogical input.
Give it something that does not add up, and it says so instead of going along with it.
The full spectrum of its reasoning.
Everything that happened on the way to the conclusion, and how the model classified each step: what it took as true, what as false.
Next on the roadmap: PRISM connects to its own reasoning and explains why it reached each statement and where the reasons come from.
Proven small. Built to grow.
Training PRISM to basic conversation would take about seven years on the hardware we have. On NVIDIA Blackwell, one training run takes about a day, so the architecture can be rebuilt and tested as often as it takes. The seed round pays for that.
On the hardware we have
How long training PRISM to basic conversation would take today.
On NVIDIA Blackwell
Per training run, so the architecture can be rebuilt and retested as often as it takes.
The seed round
Pays for that compute and the people to use it.
Proof of concept
The mechanism already works and is shown in a live demo.
About PRISM
Straight answers to what investors and partners ask us first.
No. ChatGPT predicts the next word, and nothing inside it checks whether the answer is true. PRISM scores the whole answer first, corrects contradictions before it speaks and shows how it got there.
A model that gives every possible answer a score called energy. Low energy means the answer fits; high energy means something contradicts. Reasoning becomes finding the answer with the lowest energy.
Its self-correction is: when the energy spikes past the threshold, the correction always happens before output. That is what makes its decisions repeatable and auditable.
Yes. A language model can still do the talking while PRISM does the checking.
See it working.
We show the PRISM proof of concept live, and share the technical brief and data room with qualified investors and partners.
Nothing on this website is an offer to sell or a solicitation to buy securities.