Proof, built tier by tier.
Ziqqur takes its name from a structure built tier by tier, where every level rests on the one below.
Our architecture follows the same idea. We build upward from mathematical foundations toward answers that can be traced, checked, and withheld when the evidence is not enough.
The ziggurat of math
Our architecture is built upward from formal, composable, checkable layers.
Compute-light by design
We avoid unnecessary model inference where algebraic reasoning, retrieval, and verification can do the work.
Evidence before answer
Ziqqur proves what it can, traces what it can, and abstains when the evidence is not enough.
Human authority by default
The system is designed to support human judgment with provenance, uncertainty, and auditability.
A trust problem, then and now.
In the early twentieth century, even mathematics was forced to ask what could be trusted.
New ideas about infinity had exposed paradoxes and made the foundations of the field feel less secure. David Hilbert believed trust could be restored by making reasoning more formal: statements, proofs, and rules written in a way that could be checked mechanically.
AI is now facing its own version of that problem. Large language models can produce fluent answers that are difficult to verify. Once an unsupported answer enters a workflow, every decision downstream becomes harder to trust.
Ziqqur is inspired by Hilbert’s ambition to make reasoning checkable, but shaped by the limits later mathematics revealed. We are not trying to prove everything. We build systems that prove what they can, trace what they can, and abstain when the evidence is not enough.
The ziggurat of math.
That is why we chose the name Ziqqur.
A ziggurat is built tier by tier, with each level resting on the one below it. Our architecture follows the same idea. It does not begin with a model trying to guess the most likely answer. It begins with layers of mathematics that define what the system is allowed to do.
Logic gives rules. Algebra gives composition. Information theory measures uncertainty. Spectral methods encode structure. Sheaf theory asks whether local pieces of evidence can fit together into a global answer. Lean checks the 2,447 machine-verified proofs underneath the system.
Where most AI systems begin with prediction, Ziqqur begins with structure.
Compute-light by design.
AI's compute footprint is not abstract. Training and serving large models at scale strains electrical grids and drives new data-center construction just to keep pace with demand. As adoption grows, that footprint grows with it — unless something changes in how the compute is spent.
Green AI begins with restraint. If a question can be answered through algebraic retrieval, compression, verification, or source tracing, Ziqqur should not spend compute sampling language around it.
The certified core is deterministic and sampler-free. It is designed to do the smallest sufficient computation: retrieve what is relevant, reason over structure, check the answer, and return it with provenance.
Cited retrieval benchmark: 1,750ms for Ziqqur’s deterministic path versus 7,698ms for Azure AI Search.
Median dispatch latency at full catalog coverage.
Competitive retrieval performance with a compact encoder.
Estimated inference cost per query on commodity CPU — no GPU required.
No sampler in the core
The certified core is deterministic, not built around repeated generation.
Source: Internal benchmark testing and formal verification documentation. Figures reflect current internal results.
The same structure that makes Ziqqur more verifiable also makes it lighter to run.
People remain the authority.
Human-centered AI does not mean making the machine sound more human. It means giving people systems they can inspect, challenge, and refuse.
Ziqqur is designed to preserve human judgment. The system shows where an answer came from, exposes uncertainty, and abstains when the evidence is not enough.
In that sense, abstention is not a failure state. It is a human-protective design choice. A system that says “I don’t know” at the right time is safer than one that fills the gap with a fluent guess.
Tested across four regulated industries: biotech, energy, aerospace, and finance.
Observed on the cited CRAG Task 2 evaluation; the certified core abstains when evidence is insufficient.
Output quality is evaluated across transparency, ethics, calibration, and provenance.
Certify or abstain
Answers must pass core gates before emission or be withheld.
Source-traced
Outputs remain connected to the records and extraction steps that support them.
Source: Internal benchmark testing, certification-gate documentation, and output-quality evaluation records. Figures reflect current internal results.
The system is not designed to win trust through confidence. It is designed to earn trust through evidence.
The same commitment.
Proof, restraint, and human authority are not separate values.
They come from the same architectural decision: build AI around what can be checked, traced, and supported — and stop when that support runs out.
That is the ethos behind Ziqqur.