AI-native trust infrastructure for industrial 3D printing.
3DCIPHER helps regulated manufacturers prove where a printed part came from, which printer made it, which toolchain produced it, and which evidence package an auditor can verify later. The stack combines printer-side HSM attestation, AI-based mesh watermarking and detection, digital-twin certificates, audit-assistant workflows, and a small API surface built for factory systems.
One AI product surface, three verifiable layers.
Manufacturers can adopt one module for a narrow provenance problem or deploy the full chain for regulated production evidence.
Vault3D
AI-monitored printer attestation with HSM signatures over every as-built bundle.
module details » 02MeshGuard
Neural geometry watermarking and AI detection for CAD distribution, licensed production, and counterfeit triage.
module details » 03TwinCert
AI-assisted digital-twin certificates and audit packages mapped to regulated workflows.
module details »From design file to AI-reviewed audit evidence.
Bind CAD
AI classifies CAD revision, licensee, part class, and evidence policy before slicing.
Watermark mesh
MeshGuard embeds provenance in the geometry where slicers and metadata stripping cannot remove it.
Sign bundle
Vault3D signs G-code, slicer build, firmware hash, operator session, and sensor envelope.
Issue TwinCert
AI prepares the TwinCert evidence record; customer policy signs conformance, custody, and regulatory mappings.
Export proof
AI summarizes exceptions; auditors, customers, and receiving sites verify offline against the ceremony root.
AI where it helps, signatures where authority matters.
3DCIPHER uses AI for geometry understanding, evidence extraction, anomaly triage, and audit preparation. The final authority remains cryptographic: HSM signatures, customer roots, signed manifests, and verifier output.
Neural provenance.
MeshGuard embeds and detects watermark payloads in mesh and point-cloud features, even after slicer and manufacturing transformations.
Evidence intelligence.
AI maps inspection files, material-lot records, and custody documents into TwinCert fields for human approval.
Operational anomaly detection.
Models flag unusual slicer builds, stale manifests, missing sensor envelopes, and operator-pattern deviations before they reach an audit.
Audit assistant.
AI drafts evidence summaries and exception reports, while every claim links back to a signed source artifact.
Numbers buyers can take to review.
Built for AI-assisted regulated manufacturing teams.
Aerospace and defence.
Flight-qualified additive parts need evidence that survives supplier handoffs, audits, and long retention windows.
aerospace use case »Medical and dental.
Patient-specific parts need a verifiable record linking material lot, printer state, conformance checks, and custody.
medical use case »Industrial OEMs.
Licensed spare-parts programs need watermarking and verification when third parties print under contract.
customer scenarios »Security and quality teams.
Teams that already own PKI, HSMs, SIEMs, and audit packages can integrate through the API and SDKs.
API docs »How teams start.
Demo walkthrough.
Review a signed print flow, TwinCert record, and audit package using anonymised production-shaped data.
Pilot deployment.
Connect one printer, one slicer path, and one evidence workflow with a scoped support window.
Production rollout.
Enroll fleet keys, connect IdP groups, publish the customer manifest, and train operators on runbooks.
See the product path before the security appendix.
The security material remains public, but the fastest way to evaluate fit is the demo flow, the API docs, and the pricing model.