Air Canada's AI chatbot gave a customer wrong information…
A lawyer's AI invented six court cases that never existed…
A billion AI agents arrive by 2029…
Stanford tested the AI on real legal questions…
AI has no live source to verify against…
Most models are frozen at training time, with no live feed to the world. They reason from stale memory, with nothing to verify against.
Every task starts from scratch, with nothing to build on. They can't prove what they saw or did, so recall is guesswork, not record.
The result: hallucinations, broken trust, and agents that can't operate autonomously at scale.
One fact appears after the AI training cutoff. Ten thousand people ask for it — and users have to re-discover it, from scratch, ten thousand times.
No memory of it. So the model crawls the live web — a dozen sources, parsed fresh. You wait thirty seconds.
Nobody kept the result. So everybody pays for it again.
One answer, metered to the token — then re-derived and re-billed, ten thousand times over.
Reach consensus on the fact. Verify it. Keep it. Now no one has to find it again.
Where every claim has a hash. And every hash has a consensus witness.
Real-time, blockchain-verified data meets advanced, persistent memory — so agents pull trusted facts, recall everything, and prove exactly what they know.
Instant access to live, blockchain-verified data — a source of truth that's always current, never frozen.
Perfect memory across every session — recall that compounds and hash-chains instead of resetting to zero.
Every claim hashed and witnessed on-chain — agents can prove exactly what they knew, and when they knew it.
Not an improvement — the foundational infrastructure that makes truly reliable agentic systems possible.
This is the oracle AI never had — every claim hashed, timestamped, and witnessed on-chain. A live source of truth an agent can finally check against.
On every public benchmark we ran, SupraOS memory beats the best-known system — by 13 to 17 points.
One pipeline — from edge data to verified, on-chain recall.
Data is indexed daily across a group of edge SupraOS users.
That data is promoted to Global Memory once the network reaches consensus.
It is synthesized through the Threshold AI Oracle — multi-model consensus, not a single source.
Memories are indexed with rich embeddings for fast, efficient retrieval.
AI agents tap Supra's Cognitive Modes recall system to pull facts instantly — with full provenance of the sourcing material.
Agents pay micro-fees to the network. Enterprises and frontier providers are shielded from liability.
End consumers get accurate answers faster and cheaper — no waiting on agents to re-crawl dozens of sites, no ballooning latency or token spend.
An oracle for AI takes two layers working together — a public chain that can't be tampered with, and a memory that writes every claim to it.
Together: a claim the memory makes, and a chain that proves it was never faked. That's the oracle.
Every memory hashed. Every hash witnessed. Every witness on-chain. This is the verification layer that makes AI agents reliable enough to act.
Same answer, ten thousand times over. One path makes every agent pay to re-derive it. The other proves it once — and serves it to all of them, instantly.
The proof is what lets an agent verify instead of re-derive — one query replaces ten thousand syntheses. Cheaper for everyone. Instant for everyone.
Google indexed the web for people.
AI search isn't blue links to skim — it's verified facts retrieval. Every answer comes back proven authentic, current, and true: one query, instant, trusted.
Our edge is the stack that makes it real: our own Layer-1 · a deployed oracle securing data in production · benchmark-leading memory.
Every fact the model never learned used to cost a full re-derivation. We turn it into one verified lookup: the user pays a fifth, saves the rest, and gets the answer in 100 milliseconds. We keep 20% of what we save them.
OUR FEE PER 10 POINTS +$1.83B / yr | USERS SAVE +$7.3B / yr
Base case — one in ten agents — is a $1.83B line for us, and it sits on top of ~$7.3B we just saved those users in token spend. The fee never moves; the volume does — and every point of growth saves the market four dollars for every one we keep.
We only ever charge a fifth of the value we create — so the cheaper and faster we make every lookup, the more we earn. That fifth, at a billion agents, is a multi-billion-dollar business aligned with the customer on every single query.
— cryptographic hash digests shown in fact cards are illustrative · the verification architecture they represent is real · live hashes appear on-chain at the supra explorer —
Everything above describes a global, public substrate. But the same architecture pays for itself long before any of that exists — inside a single company.
The redundancy isn't unique to the open web; behind the firewall it's often worse. A company running hundreds of agents across fifty people has the same problem at smaller scale. One rep's agent researches a customer on Monday; another rep's agent researches the same customer on Thursday; a third does it again next week. Legal re-derives the same clause. Security re-investigates the same CVE. Every redo is tokens spent re-buying an answer the company already paid for.
Supra Cognitive Modes runs locally, over a company's own processed data, with nothing leaving its walls. When an agent does real work, the result is stored, scored, and provenance-tagged — so the next agent retrieves it instead of recomputing it. The classifier routes internal questions exactly as it routes everything else:
The win here is a line item, not a someday: lower token spend, faster agents, less duplicated work, and more consistent answers across teams. That's a number an operator can point at this quarter — not a network they have to wait for the world to join.
The ledger has a private form too. An enterprise can run its own audit chain — its own hashes and timestamps, with no public network involved. The point there isn't decentralization; it's governance: an immutable record of which sources produced an answer, which model generated it, what was known at the time, and whether anything changed since. For finance, healthcare, insurance, and government, that provenance trail is fast becoming a compliance requirement rather than a nicety.
That ordering is the point. The first layer pays for itself inside one company. The second adds governance the regulated industries already want. The third lets organizations that don't trust each other reuse knowledge anyway. And the global layer becomes a natural expansion of the first three — not a prerequisite for any of them.
We don't need the whole world to adopt a protocol for this to be worth building. We need one company tired of paying twice for the same answer — and there are a great many of those.