AI can draft a filing, plan a tool call, design a protein, write a policy, produce audit evidence, and make a claim someone will act on. None of that is verified until it survives structure. Invariant is the proof boundary between AI output and real-world consequence. SIGMA checks the structure. SATYA applies it to live workflows. Every check produces a signed receipt.
The infrastructure of trust for streaming data and evolving AI state.
Every framework says "verify AI output." None of them say how.
From biotech to compliance, from the EU AI Act to cross-examination, the requirement is everywhere. The mechanism is missing.
The result: policies that say "verify" sitting on top of workflows that hope. A secondary review that depends on a human catching what the AI invented. Confidence scores that look like verification but survive nothing: not a regulatory audit, not a malpractice claim, not a peer review.
SATYA is the mechanism. Deterministic. Zero parameters. Cryptographic receipts. It checks whether every claim, structure, or policy coheres with its source evidence before it goes anywhere.
Where It Breaks
One failure pattern. Many regulated surfaces. One verifier.
ProteinPrediction confidence is not coherenceAlphaFold2 pLDDT assigns high confidence to 33.6% of fold-switching proteins that are wrong. pLDDT is the model grading itself. SATYA tests whether the structure actually coheres: backbone bonds, native contacts, disulfide geometry, steric exclusions, all checked as a single system. If the constraints contradict, you know which residues and why.
LegalReview processes miss fabricated citationsSullivan & Cromwell had AI policies, mandatory training, and secondary review. Fabricated citations still reached a federal docket. A Georgia prosecutor was suspended for relying on ten AI-generated citations in a murder case. SATYA searches the record and verifies support before the output leaves the building.
GovernanceAudit evidence gaps hide until the auditor finds themAn HR policy, a SOC 2 control, and an audit finding may each look correct in isolation. SATYA checks whether they are consistent and complete: which controls are covered, which have gaps, and where assertions contradict the evidence. The signed receipt is the audit artifact.
Every verification produces a signed receipt: what was checked, what the evidence actually says, and whether the output is safe to rely on. Signed with Ed25519. Independently verifiable. Same input, same result, every time.
The same pattern appears anywhere AI output becomes action: contracts, procurement evidence, insurance decisions, scientific claims, compliance controls, agent tool calls, and protein candidates. Generated is not verified until the structure survives.
The same failure keeps appearing in different clothes.
Legal does not have a writing problem. It has an obligation-verification problem.Contracts mutate duties, exceptions, deadlines, citations, and risk. AI can draft the clause. SATYA verifies whether the clause survives the record.
AI governance does not have a policy problem. It has an evidence problem.Agents mutate decisions, tool calls, permissions, and audit trails. SATYA turns every governed action into a signed receipt.
Procurement does not have a vendor-profile problem. It has a trust-chain problem.Vendors mutate risk, certifications, exceptions, and contract terms. SATYA verifies what is claimed, what is missing, and what contradicts the evidence.
Scientific computing does not have a paper problem. It has a claim-integrity problem.Models mutate structured hypotheses. SATYA verifies whether the structure still coheres before the result becomes a publication, filing, or downstream dataset.
Protein verification does not have a confidence-score problem. It has a structure-routing problem.Prediction models generate candidates. SATYA verifies whether the candidate is structurally coherent before synthesis, partner review, or database ingestion.
You thought the problem was the writing. It is the verification. The current tools compress risk into a score. SATYA decomposes the failure and signs the evidence.
What SATYA Returns
Not a score. A map, a localization, a receipt, and a trace.
CoverageCoverage MapWhich claims were checked, which sources were matched, and which assertions have no supporting evidence. You see exactly what was verified and what was not.
LocalizationFailure LocalizationWhen something fails, SATYA tells you exactly where: which residue, which citation, which clause, which policy control. Not "something is wrong." Where it is wrong and why.
ArtifactSigned ReceiptEd25519 cryptographic signature binding the input hash, source hashes, verdict, and timestamp. Independently verifiable by any third party. The compliance artifact.
ReproducibilityDeterministic TraceSame input, same result, every time. No stochastic variation, no model drift, no version sensitivity. The verification is a mathematical proof, not an opinion.
How It Works
Trust infrastructure for systems whose state is constantly changing.
AI systems do not just generate outputs anymore. They mutate state: claims, plans, graphs, policies, evidence, structures, and decisions. The failure is not only that AI generated something false. The failure is that no one is maintaining global trust as the underlying data changes.
EngineSIGMAMaintains structural coherence as state changes. O(1) amortized per edit, verified at 5M vertices (35 µs median, scaling exponent 0.19, drift = 0).
WorkflowSATYAApplies checks to live workflows: agents, protein, compliance, legal, scientific computing.
ReceiptSVRTurns every check into a signed, portable, independently verifiable artifact. Ed25519.
TransparencyLocusAnchors receipts into a public transparency log. Hash-only. Independently resolvable.
Together, they are trust infrastructure for streaming data.
SATYA is not a confidence score. It is not "our model thinks your model might be wrong." It is a mathematical verification engine built on cellular sheaf cohomology that sits underneath your existing AI systems. Your LLMs generate. Your RAG retrieves. SATYA verifies the structural consistency of the output against source evidence, deterministically, before it reaches anyone.
The verification itself uses zero learned parameters, zero GPU, and zero training data. That is not a limitation. That is the point: the verifier cannot hallucinate because there is no generative model in the verification loop. It complements your probabilistic stack with a deterministic proof layer.
The output is signed with Ed25519. The input hash, source hashes, verification dimensions, and timestamp are bound to a cryptographic signature. The receipt is independently verifiable by any third party with the public key. That is the kind of evidence that survives cross-examination, regulatory inquiry, and peer review, because both the math and the signature are independently auditable.
35 µsper streaming edit at V=5M
5Mvertices, drift = 0
0parameters, 0 GPU
Ed25519signed receipts
The Next Race
The next buyer question will not be: can your AI generate it?
Everyone will generate filings, policies, audit evidence, vendor reviews, agent plans, and protein candidates.
The buyer question will be: what did the system verify before anyone relied on it?
A chat transcript is not an answer. A confidence score is not an answer. A policy saying "human review required" is not an answer.
A signed verification receipt is an answer.
Generation became cheap. Action is becoming cheap. Trust was the missing layer.
SIGMA makes trust computational.
The industry is building the compute infrastructure for agents. SIGMA is building the trust infrastructure for agents.
The Landscape
Generation is a commodity. Trust infrastructure is the moat.
An open-source Harvey replica launched on GitHub and hit 927 stars in 48 hours. Any developer with an API key can build a legal AI product this weekend. The generation layer is free. Every firm, every platform, every vendor will have it.
What they will not have: a deterministic verification engine that produces a cryptographic receipt on every answer. A receipt that proves what was checked, what the sources actually say, and whether the output is faithful. That receipt is the difference between "we had a policy" and "here is the proof."
Colorado SB 24-205 requires deployers of high-risk AI systems to demonstrate "reasonable care." The EU AI Act Article 15 requires accuracy and robustness measures with audit trails. SATYA generates the compliance artifact that answers both.
"Verification is the new product moat. Autonomy expands only where verification expands."