Introducing
Deterministic intelligence for AI models and agents you already run
Same model. Provably accurate. Smaller bill.
The problem
AI has hit a capital wall
$700B+
Capital expenditure planned for 2026 by the four largest hyperscalers, Amazon, Alphabet, Microsoft and Meta.
SourceQ3 2026 earnings releases of Amazon, Alphabet, Microsoft and Meta; each defines capital expenditure slightly differently.Where the cost hides
Your model keeps re‑reading
Today the AI model finds its own context. It searches, reads and searches again, and the same material passes through it repeatedly. Every pass adds cost.
In agentic AI calls in September 2026, just under 90% of all tokens were cached prompt tokens, context the model had already read.
SourceOpenRouter, September 2026. What Axiom Vertex helps you solveHow it works
One coordinate per record
Each record is validated once and identified by a 48-byte coordinate that never drifts, and the record stays in your storage. Conventional retrieval compares a full embedding vector for every record, thousands of numbers or more.
341×
less data compared per record. Over 834 depth layers, 13.7 MB against 40 KB.
Method4 bytes per dimension for a conventional embedding vector against one 48-byte coordinate, per record at every depth layer.Deterministic
Same context on any chip
Conventional retrieval can pull different records on every run. Axiom Vertex works on validated records and exact integer arithmetic, so the same question returns the same context and the same validation proofs, bit for bit, on any hardware. The AI model reasons over drift-free context on every run, and with deterministic decoding it returns the same answer.
On what date was invoice 4471 paid?
From the Axiom Vertex Reasoning Model, run with the same AI model in both lanes.
Our mission
The foundation for economically viable AI
Making artificial intelligence affordable and safe at any scale