https://illuminis.ai/assets/HowInferTrustWorks-B90yJUr2.js

1import{j as e,S as f,H as l,G as c,h as r,a as d,s as n,b}from"./index-NhrPNU5X.js";import{A as a,_ as v,ah as m,b2 as w,e as j,a4 as x,G as p,cb as N,cc as T,au as I,c as k,z as g,bl as A,a9 as o,aS as S,ab as D,aa as E,bZ as L,bN as C,W as H,l as P,L as R}from"./icons-DIYs2NZR.js";import{L as s}from"./vendor-J5X5jj06.js";import"./seo-CRw9-zMO.js";const h=[{number:1,title:"Capture",icon:v,color:"primary",details:["InferTrust™ intercepts the inference at the execution boundary, the exact point where the model produces its output, before any downstream system acts on the result.","The capture operation records six fields: model version hash, input feature hash, confidence score, decision action, active policy hash, and timestamp. A monotonic Sequence ID is also appended to detect gaps or reordering.","Critically, InferTrust™ does NOT copy raw input data. Only cryptographic hashes of the input features are stored. The original data never leaves the inference pipeline through InferTrust™, preserving data minimization requirements under HIPAA, GDPR, and similar frameworks."]},{number:2,title:"Sign",icon:m,color:"accent",details:["A device-bound cryptographic key signs the captured record. This key is stored in a hardware security module: TPM 2.0, Apple Secure Enclave, ARM TrustZone, or a dedicated HSM, depending on the deployment environment.","Signing happens on the local device before any network transmission occurs. The key is bound to the physical hardware, not to a software certificate that could be exported or cloned.","This hardware binding is what makes records non-repudiable. No one, not even the device operator, can forge a signature from a different device. Every record carries proof of which specific hardware instance produced it."]},{number:3,title:"Seal",icon:w,color:"purple",details:["The signed record is appended to a tamper-evident, append-only log. Each entry chains to the previous entry's hash, creating a verifiable sequence similar to a blockchain but without the overhead of distributed consensus.","Deleting or reordering records breaks the hash chain and is immediately detectable by any party that holds a copy of the chain. Sequence gaps prove records were removed.","The sealed log can reside on local storage, on a network share, or be replicated to a central audit store. Regardless of where it lives, the cryptographic chain ensures integrity."]},{number:4,title:"Verify",icon:j,color:"primary",details:["Any authorized party can independently verify any decision record. Verification checks four properties: signature validity, chain integrity, sequence continuity, and policy version match.","No special software is needed beyond standard cryptographic libraries. InferTrust™ uses well-established algorithms (ECDSA P-256, SHA-256) that any auditor or third-party tool can validate.","Verification works entirely offline. An auditor can take a copy of the sealed log, disconnect from any network, and still verify every record in the chain."]}],q=[{icon:x,abbrev:"MID",title:"Model Version ID",description:'SHA-256 hash of the model binary. This is not a human-readable version string like "v2.3" but a cryptographic fingerprint that uniquely identifies the exact weights, architecture, and training run. If a single weight changes, the hash changes.'},{icon:p,abbrev:"CS",title:"Confidence Score",description:"The float value the model assigned to its output. This is the value used for confidence gating: decisions above the threshold execute autonomously, while decisions below the threshold escalate to a human reviewer."},{icon:N,abbrev:"PVH",title:"Policy Version Hash",description:"SHA-256 hash of the policy ruleset active at decision time. Policies define thresholds, guardrails, and routing rules. Because the hash is sealed with the record, you can always prove which rules governed any specific decision."},{icon:T,abbrev:"IFH",title:"Input Feature Hash",description:"SHA-256 hash of the input tensor or feature vector. This proves what the model consumed without storing the raw data. In privacy-sensitive domains, this is essential: you can demonstrate provenance without exposing patient records, financial data, or PII."},{icon:I,abbrev:"TS",title:"Timestamp",description:"Monotonic clock value, cryptographically bound to the decision record through the signature. Because it is part of the signed payload, the timestamp cannot be altered independently. Backdating a record would invali

1date its signature."},{icon:k,abbrev:"DA",title:"Decision Action",description:"An enumerated value indicating the operational outcome: APPROVE, ESCALATE, DENY, or FLAG. Both the model prediction and the downstream action are sealed together, so there is no ambiguity about what the system actually did."},{icon:g,abbrev:"SEQ",title:"Sequence ID",description:"A strictly monotonic uint64 counter. Each record increments the counter by exactly one. Any gap in the sequence proves a record was deleted. Any duplicate proves a record was inserted. This is the mechanism that makes completeness verifiable."}],M=[{dimension:"Record integrity",logging:"Mutable. Log files can be edited, truncated, or deleted by anyone with file system access.",infertrust:"Immutable. Each record is cryptographically signed and chained. Any modification is detectable."},{dimension:"Signing location",logging:"Centralized. Logs are written by the application server, often after network transmission.",infertrust:"Edge-signed. Cryptographic signature is created on the device running the model, before any network hop."},{dimension:"Verification",logging:"No built-in verification. You must trust that the log has not been altered.",infertrust:"Any party can verify any record using standard cryptographic libraries. No trust assumptions required."},{dimension:"Tamper evidence",logging:"None. Edits leave no trace unless a separate integrity monitoring system is in place.",infertrust:"Hash chaining and sequence IDs make deletions, insertions, and reordering immediately detectable."},{dimension:"Completeness",logging:"Best-effort. Dropped records, partial writes, and log rotation gaps are common under load.",infertrust:"Monotonic Sequence IDs prove completeness. Missing records produce detectable gaps."},{dimension:"Regulatory acceptance",logging:"Routinely challenged. Log files lack the integrity guarantees regulators and courts expect.",infertrust:"Cryptographic evidence meets evidentiary standards for FDA, HIPAA, SEC, and EU AI Act compliance."}];function z(){return e.jsxs(e.Fragment,{children:[e.jsx(f,{title:"How InferTrust™ Works | Architecture and Technical Deep Dive",description:"InferTrust™ creates cryptographic proof at the moment of AI inference. Learn the four-step process: capture, sign, seal, and verify. Sub-millisecond overhead, offline-capable, tamper-evident.",path:"/solutions/how-infertrust-works",jsonLd:[{"@context":"https://schema.org","@type":"WebPage",name:"How InferTrust™ Works | Architecture and Technical Deep Dive",description:"InferTrust™ creates cryptographic proof at the moment of AI inference. Learn the four-step process: capture, sign, seal, and verify. Sub-millisecond overhead, offline-capable, tamper-evident.",url:"https://illuminis.ai/solutions/how-infertrust-works",publisher:{"@type":"Organization",name:"illuminis",url:"https://illuminis.ai"}},{"@context":"https://schema.org","@type":"BreadcrumbList",itemListElement:[{"@type":"ListItem",position:1,name:"Home",item:"https://illuminis.ai/"},{"@type":"ListItem",position:2,name:"Solutions",item:"https://illuminis.ai/solutions"},{"@type":"ListItem",position:3,name:"InferTrust™",item:"https://illuminis.ai/solutions/infertrust"},{"@type":"ListItem",position:4,name:"How It Works"}]}]}),e.jsxs("section",{className:"bg-gradient-to-br from-dark-900 via-dark-800 to-primary-900 text-white py-20 lg:py-28 relative overflow-hidden",children:[e.jsx(l,{}),e.jsx(c,{color:"purple",size:"lg",animate:!0,className:"top-10 right-10"}),e.jsx(c,{color:"accent",size:"md",animate:!0,className:"bottom-10 left-20"}),e.jsx("div",{className:"max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 relative",children:e.jsxs("div",{className:"max-w-4xl",children:[e.jsx("p",{className:"text-primary-400 font-semibold text-sm uppercase tracking-wider mb-4",children:"Technical Deep Dive"}),e.jsx("h1",{className:"text-4xl sm:text-5xl font-extrabold tracking-tight",children:"How InferTrust™ Works"}),e.jsx("p",{className:"mt-6 text-xl text-gray-300",children:"InferTrust™ creates a cryptographic receipt at the exact moment an AI model produces a decision. The receipt is signed on the local device, sealed into a tamper-evident log, and available for verification by any authorized party. Here is exactly how each step works."}),e.jsxs("div",{className:"mt-8 flex flex-col sm:flex-row gap-4",children:[e.jsxs(s,{to:"/contact?interest=technical-demo",className:"inline-flex items-center justify-center px-8 py-4 text-lg font-semibold rounded-lg bg-primary-600 hover:bg-primary-500 text-white transition-colors",children:["Request a Technical Demo",e.jsx(a,{className:"ml-2 h-5 w-5"})]}),e.jsx(s,{to:"/solutions/infertrust",className:"inline-flex items-center justify-center px-8 py-4 text-lg font-semibold rounded-lg border border-white/20 hover:bg-white/10 text-white transition-colors",children:"InferTrust™ Overview"})]})]})})]}),e.jsx(r,{}),e.jsx("section",{className:"py-20 bg-white",children:e.jsxs("div",{className:"max-w-7xl mx-auto px-4 sm:px-6 lg:px-8",children:[e.jsxs("div",{className:"text-center mb-16",children:[e.jsx("h2",{className:"text-3xl sm:text-4xl font-bold text-gray-900",children:"The Four-Step Process"}),e.jsx("p",{className:"mt-4 text-lg text-gray-600 max-w-3xl mx-auto",children:"Every AI decision passes through four stages: capture, sign, seal, and verify. Each stage adds a layer of cryptographic assurance that the record 

1is complete, authentic, and tamper-evident."})]}),e.jsx("div",{className:"space-y-8",children:h.map((t,i)=>e.jsxs("div",{className:"relative",children:[e.jsxs("div",{className:"flex flex-col lg:flex-row gap-6 lg:gap-10 items-start",children:[e.jsx("div",{className:"flex-shrink-0 flex items-center gap-4",children:e.jsx("div",{className:`w-20 h-20 rounded-2xl flex items-center justify-center text-white font-extrabold text-3xl ${t.color==="primary"?"bg-gradient-to-br from-primary-500 to-primary-700":t.color==="accent"?"bg-gradient-to-br from-accent-500 to-accent-700":"bg-gradient-to-br from-purple-500 to-purple-700"}`,children:t.number})}),e.jsxs(d,{hover:!1,className:"p-8 flex-1",children:[e.jsxs("div",{className:"flex items-center gap-3 mb-4",children:[e.jsx(t.icon,{className:`h-7 w-7 ${t.color==="primary"?"text-primary-600":t.color==="accent"?"text-accent-600":"text-purple-600"}`}),e.jsx("h3",{className:"text-2xl font-bold text-gray-900",children:t.title})]}),e.jsx("div",{className:"space-y-3",children:t.details.map((u,y)=>e.jsx("p",{className:"text-gray-600 leading-relaxed",children:u},y))})]})]}),i<h.length-1&&e.jsx("div",{className:"hidden lg:block ml-10 h-8 w-px bg-gradient-to-b from-primary-300 to-accent-300"})]},t.number))})]})}),e.jsx(r,{}),e.jsx("section",{className:"py-20 bg-gray-50",children:e.jsxs("div",{className:"max-w-7xl mx-auto px-4 sm:px-6 lg:px-8",children:[e.jsxs("div",{className:"text-center mb-16",children:[e.jsx("h2",{className:"text-3xl sm:text-4xl font-bold text-gray-900",children:"What Gets Recorded"}),e.jsx("p",{className:"mt-4 text-lg text-gray-600 max-w-3xl mx-auto",children:"Every InferTrust™ decision record contains exactly seven fields. Together, these fields answer every question a regulator, auditor, or court will ask: what model, what input, what confidence, what policy, what outcome, when, and in what order."})]}),e.jsx("div",{className:"grid md:grid-cols-2 lg:grid-cols-3 gap-6",children:q.map((t,i)=>e.jsxs(d,{hover:!0,accentColor:b(i),className:"p-8 h-full",children:[e.jsxs("div",{className:"flex items-center gap-3 mb-4",children:[e.jsx("div",{className:`inline-flex p-3 rounded-xl ${n(i).softBg}`,children:e.jsx(t.icon,{className:`h-6 w-6 ${n(i).fg}`})}),e.jsxs("div",{children:[e.jsx("span",{className:`text-xs font-mono font-bold ${n(i).fg} block`,children:t.abbrev}),e.jsx("h3",{className:"text-lg font-bold text-gray-900",children:t.title})]})]}),e.jsx("p",{className:"text-gray-600 text-sm leading-relaxed",children:t.description})]},t.abbrev))}),e.jsx("div",{className:"mt-8 text-center",children:e.jsxs(s,{to:"/solutions/ai-audit-trail",className:"inline-flex items-center text-primary-600 font-semibold hover:text-primary-500 transition-colors",children:["Learn more about AI audit trails",e.jsx(a,{className:"ml-2 h-4 w-4"})]})})]})}),e.jsx(r,{}),e.jsx("section",{className:"py-20 bg-white",children:e.jsx("div",{className:"max-w-7xl mx-auto px-4 sm:px-6 lg:px-8",children:e.jsxs("div",{className:"grid lg:grid-cols-2 gap-12 items-center",children:[e.jsxs("div",{children:[e.jsx("h2",{className:"text-3xl sm:text-4xl font-bold text-gray-900 mb-6",children:"Confidence-Gated Autonomous Approval"}),e.jsx("p",{className:"text-lg text-gray-600 mb-4",children:"Organizations define confidence thresholds that determine when AI can act autonomously and when it must escalate to a human reviewer. InferTrust™ enforces these thresholds and creates cryptographic proof of every routing decision."}),e.jsx("p",{className:"text-lg text-gray-600 mb-4",children:"When the model's confidence score exceeds the threshold, the decision executes autonomously. The signed record proves the confidence level justified autonomous action under the active policy."}),e.jsx("p",{className:"text-lg text-gray-600 mb-6",children:"When the confidence score falls below the threshold, InferTrust™ routes the decision to a human reviewer. The signed record proves that escalation occurred, when it occurred, and at what confidence level. This protects both the organization and the reviewer."}),e.jsx("ul",{className:"space-y-3",children:[{icon:A,text:"Different thresholds per decision type, per department, per risk tier"},{icon:g,text:"Thresholds are version-controlled and bound to the policy hash"},{icon:o,text:"Above threshold: AI acts autonomously with signed proof"},{icon:S,text:"Below threshold: AI escalates to human with signed proof of escalation"},{icon:D,text:"Threshold changes generate new policy version hashes for traceability"}].map(t=>e.jsxs("li",{className:"flex items-start gap-3",children:[e.jsx(t.icon,{className:"h-5 w-5 text-primary-500 mt-0.5 shrink-0"}),e.jsx("span",{className:"text-gray-700",children:t.text})]},t.text))})]}),e.jsx("div",{className:"bg-gradient-to-br from-primary-50 to-accent-50 rounded-2xl p-10",children:e.jsxs("div",{className:"space-y-6",children:[e.jsxs("div",{className:"text-center mb-6",children:[e.jsx(p,{className:"h-16 w-16 text-primary-600 mx-auto mb-3"}),e.jsx("p",{className:"text-xl font-bold text-gray-900",children:"Confidence Gating"})]}),e.jsxs("div",{className:"bg-white rounded-xl p-5 border border-green-200",children:[e.jsxs("div",{className:"flex items-center gap-3 mb-2",children:[e.jsx("div",{className:"w-3 h-3 rounded-full bg-green-500"}),e.jsx("span",{className:"font-semibold text-gray-900",children:"Above Threshold"})]}),e.jsx("p",{className:"text-sm text-gray-600",children:"Autonomous execution. Signed re

1cord proves confidence justified action."})]}),e.jsxs("div",{className:"bg-white rounded-xl p-5 border border-amber-200",children:[e.jsxs("div",{className:"flex items-center gap-3 mb-2",children:[e.jsx("div",{className:"w-3 h-3 rounded-full bg-amber-500"}),e.jsx("span",{className:"font-semibold text-gray-900",children:"Below Threshold"})]}),e.jsx("p",{className:"text-sm text-gray-600",children:"Escalation to human reviewer. Signed record proves escalation occurred."})]}),e.jsxs("p",{className:"text-xs text-gray-500 text-center",children:["Both paths produce cryptographic proof. Learn more about"," ",e.jsx(s,{to:"/solutions/ai-decision-integrity",className:"text-primary-600 hover:underline",children:"AI decision integrity"}),"."]})]})})]})})}),e.jsx(r,{}),e.jsxs("section",{className:"py-20 bg-dark-900 text-white relative overflow-hidden",children:[e.jsx(l,{spacing:80}),e.jsxs("div",{className:"max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 relative",children:[e.jsxs("div",{className:"text-center mb-16",children:[e.jsx("h2",{className:"text-3xl sm:text-4xl font-bold",children:"Architecture and Deployment"}),e.jsx("p",{className:"mt-4 text-lg text-gray-400 max-w-3xl mx-auto",children:"InferTrust™ is designed for edge deployment. Signing happens on the device running the model, with no cloud dependency for cryptographic operations or local logging."})]}),e.jsx("div",{className:"grid md:grid-cols-2 lg:grid-cols-3 gap-6",children:[{icon:E,title:"Edge-First Signing",description:"Cryptographic signing occurs on the device executing the model. GPU servers, edge devices, PLCs, medical devices, and vehicles all sign locally before transmitting any data."},{icon:L,title:"Offline Capable",description:"No cloud dependency for signing or logging. Records accumulate locally and sync to a central audit store when connectivity becomes available. The cryptographic chain remains intact regardless."},{icon:o,title:"Sub-Millisecond Overhead",description:"Hardware-accelerated cryptographic operations via TPM 2.0, ARM TrustZone, or Intel SGX add sub-millisecond latency to the inference pipeline. Performance impact is negligible even in real-time systems."},{icon:C,title:"Flexible Storage",description:"Sealed logs can reside on local disk, network storage, or cloud object stores. The hash chain guarantees integrity regardless of the storage backend."},{icon:H,title:"Central Sync",description:"When connectivity is available, local logs replicate to a central audit store for aggregated reporting, cross-device verification, and long-term retention."},{icon:m,title:"Hardware Key Binding",description:"Signing keys are bound to physical hardware (TPM, Secure Enclave, HSM). Keys cannot be exported, cloned, or migrated. Each device has a unique, verifiable identity."}].map(t=>e.jsxs("div",{className:"p-6 rounded-xl border border-white/10 bg-white/5",children:[e.jsx("div",{className:"inline-flex p-3 rounded-xl bg-primary-500/20 mb-4",children:e.jsx(t.icon,{className:"h-6 w-6 text-primary-400"})}),e.jsx("h3",{className:"text-lg font-bold text-white mb-2",children:t.title}),e.jsx("p",{className:"text-sm text-gray-400",children:t.description})]},t.title))}),e.jsx("div",{className:"mt-10 text-center",children:e.jsxs("p",{className:"text-gray-400",children:["InferTrust™ deploys across"," ",e.jsx(s,{to:"/solutions/infertrust/clinical",className:"text-primary-400 hover:underline",children:"healthcare"}),","," ",e.jsx(s,{to:"/solutions/infertrust/payer",className:"text-primary-400 hover:underline",children:"payer"}),","," ",e.jsx(s,{to:"/solutions/infertrust/financial",className:"text-primary-400 hover:underline",children:"financial"}),","," ",e.jsx(s,{to:"/solutions/infertrust/auto",className:"text-primary-400 hover:underline",children:"automotive"}),", and"," ",e.jsx(s,{to:"/solutions/infertrust/industrial",className:"text-primary-400 hover:underline",children:"industrial"})," ","environments."]})})]})]}),e.jsx(r,{}),e.jsx("section",{className:"py-20 bg-white",children:e.jsx("div",{className:"max-w-7xl mx-auto px-4 sm:px-6 lg:px-8",children:e.jsxs("div",{className:"grid lg:grid-cols-2 gap-12 items-center",children:[e.jsxs("div",{children:[e.jsx("h2",{className:"text-3xl sm:text-4xl font-bold text-gray-900 mb-6",children:"Integration"}),e.jsx("p",{className:"text-lg text-gray-600 mb-4",children:"The InferTrust™ SDK wraps your existing model inference pipeline. No model changes are required. The SDK intercepts the inference at the execution boundary, captures the decision record, signs it, and appends it to the sealed log."}),e.jsx("p",{className:"text-lg text-gray-600 mb-6",children:"Typical integration takes days, not months. The SDK provides a thin layer between your model runtime and the InferTrust™ signing engine. Your model code, training pipeline, and deployment infrastru

1cture remain unchanged."}),e.jsx("ul",{className:"space-y-3",children:[{icon:P,text:"Python SDK for direct integration with model inference code"},{icon:R,text:"REST API and gRPC for language-agnostic deployments"},{icon:x,text:"Works with any ML framework: PyTorch, TensorFlow, ONNX, or custom runtimes"},{icon:o,text:"No model retraining, no architecture changes, no new dependencies in your model code"}].map(t=>e.jsxs("li",{className:"flex items-start gap-3",children:[e.jsx(t.icon,{className:"h-5 w-5 text-primary-500 mt-0.5 shrink-0"}),e.jsx("span",{className:"text-gray-700",children:t.text})]},t.text))}),e.jsx("div",{className:"mt-6",children:e.jsxs(s,{to:"/solutions/ai-governance",className:"inline-flex items-center text-primary-600 font-semibold hover:text-primary-500 transition-colors",children:["Learn about AI governance with InferTrust™",e.jsx(a,{className:"ml-2 h-4 w-4"})]})})]}),e.jsxs("div",{className:"bg-gradient-to-br from-dark-800 to-dark-900 rounded-2xl p-8 font-mono text-sm text-gray-300",children:[e.jsxs("div",{className:"mb-3 flex items-center gap-2",children:[e.jsx("div",{className:"w-3 h-3 rounded-full bg-red-500"}),e.jsx("div",{className:"w-3 h-3 rounded-full bg-yellow-500"}),e.jsx("div",{className:"w-3 h-3 rounded-full bg-green-500"})]}),e.jsx("pre",{className:"overflow-x-auto whitespace-pre leading-relaxed",children:`# Install the InferTrust SDK
2pip install infertrust
3
4# Wrap your existing inference
5from infertrust import InferTrust
6
7it = InferTrust(
8    model_path="model.onnx",
9    policy_path="policy.yaml"
10)
11
12# Your inference call, now signed
13result = it.infer(input_features)
14
15# result.decision  -> "APPROVE"
16# result.receipt   ->

16 signed record
17# result.seq_id    -> monotonic ID`})]})]})})}),e.jsx(r,{}),e.jsx("section",{className:"py-20 bg-gray-50",children:e.jsxs("div",{className:"max-w-7xl mx-auto px-4 sm:px-6 lg:px-8",children:[e.jsxs("div",{className:"text-center mb-16",children:[e.jsx("h2",{className:"text-3xl sm:text-4xl font-bold text-gray-900",children:"InferTrust™ vs. Traditional Logging"}),e.jsx("p",{className:"mt-4 text-lg text-gray-600 max-w-3xl mx-auto",children:"Traditional logging was designed for debugging, not for evidence. Here is how InferTrust™ compares on the dimensions that matter for compliance, litigation, and regulatory response."})]}),e.jsx("div",{className:"overflow-x-auto",children:e.jsxs("table",{className:"w-full border-collapse bg-white rounded-xl overflow-hidden shadow-sm",children:[e.jsx("thead",{children:e.jsxs("tr",{className:"bg-primary-600 text-white",children:[e.jsx("th",{className:"px-6 py-4 text-left text-sm font-semibold",children:"Dimension"}),e.jsx("th",{className:"px-6 py-4 text-left text-sm font-semibold",children:"Traditional Logging"}),e.jsx("th",{className:"px-6 py-4 text-left text-sm font-semibold",children:"InferTrust™"})]})}),e.jsx("tbody",{children:M.map((t,i)=>e.jsxs("tr",{className:`${i%2===0?"bg-white":"bg-gray-50"} hover:bg-primary-50 transition-colors`,children:[e.jsx("td",{className:"px-6 py-4 text-sm font-semibold text-primary-700",children:t.dimension}),e.jsx("td",{className:"px-6 py-4 text-sm text-gray-600",children:t.logging}),e.jsx("td",{className:"px-6 py-4 text-sm text-gray-600",children:t.infertrust})]},t.dimension))})]})})]})}),e.jsx("section",{className:"py-20 bg-gradient-to-r from-primary-600 to-accent-600",children:e.jsxs("div",{className:"max-w-4xl mx-auto px-4 sm:px-6 lg:px-8 text-center",children:[e.jsx("h2",{className:"text-3xl sm:text-4xl font-bold text-white mb-6",children:"See InferTrust™ in Your Environment"}),e.jsx("p",{className:"text-xl text-white/90 mb-10",children:"Request a technical demo and see how InferTrust™ integrates with your existing ML pipeline. We will walk through capture, signing, sealing, and verification using your own model architecture."}),e.jsxs(s,{to:"/contact?interest=technical-demo",className:"inline-flex items-center justify-center px-8 py-4 text-lg font-semibold rounded-lg bg-white text-primary-700 hover:bg-gray-100 transition-colors shadow-lg",children:["Request a Technical Demo",e.jsx(a,{className:"ml-2 h-5 w-5"})]})]})})]})}export{z as default};