Deterministic verification infrastructure for AI governance.
DigiEmu Core & Proof provides reproducible decision states, execution receipts, replay verification and audit-ready evidence bundles for accountable AI systems.
Snapshot / Replay / Verify
Open Standard. Commercial Infrastructure.
Transparent core standard. Professional enterprise adoption.
DigiEmu separates the public standard layer from commercial enterprise adoption. The Core specification is designed to be transparent, reviewable and interoperable. Baumgartner Digital Infrastructure provides the commercial infrastructure required for secure enterprise deployment, conformance testing, audit reporting, integration and support.
Proof concept
From AI decision to verifiable evidence.
Input
AI-assisted decision input
State
canonical_decision_state: reproducible
Hash
sha256: 8f4c...a91e
Verify
PASS
BFH / Innosuisse project fit
From concept to validated applied research prototype.
Scientific contribution
Formalize deterministic knowledge-state boundaries, canonical snapshots and replay-verifiable transition chains for AI governance.
Applied prototype
Develop DigiEmu Proof as a working verification prototype that can generate PASS / FAIL evidence reports.
Use case validation
Evaluate the approach in regulated workflows such as medical AI, compliance, finance or public-sector AI governance.
Market relevance
Prepare DigiEmu for practical adoption as lightweight verification infrastructure for organizations using AI-assisted decisions.
Current validation tracks
Concrete partner-facing validation paths.
GazaCare AI
Medical AI traceability: decision fingerprint, QR/hash evidence and verifiable recommendation history.
Public Sector / CO₂
Decision-state reconstruction for public-sector workflows, climate reporting and accountable administrative AI.
TBN
Complementary trust architecture: TBN verifies agent trust; DigiEmu reconstructs decision state.
Antifragile.AI
Potential governance interoperability: DigiEmu as deterministic evidence layer for AI approval workflows.
Core 2.0 draft.4
Technical review candidate for BFH / partner feedback.
DigiEmu Core 2.0 draft.4 is positioned as a partner-testable milestone for reviewing deterministic snapshots, replay verification, schema boundaries, conformance reports and audit evidence bundles.
Request technical review packageResearch problem
AI systems are documented, but their decision states are rarely reconstructable.
Regulated AI workflows need evidence that can be independently checked. Logs, screenshots and explanations are useful, but they do not automatically prove that a specific AI-assisted decision state can be reconstructed, replayed and verified.
Deterministic state
Define exactly what belongs inside the reproducible knowledge boundary.
Transition integrity
Verify that one state correctly follows another through a receipt-backed transition.
Audit evidence
Generate PASS/FAIL reports that make verification concrete and inspectable.
A deterministic standard for AI decision-state boundaries.
Core describes how AI-related knowledge states are captured, canonicalized and separated from non-deterministic metadata. It defines what must be reproducible, what stays outside the hash and how AI governance evidence can be structured.
A minimal verifier for deterministic execution integrity.
Proof checks whether snapshots, receipts and transition chains compose into a verifiable result. The output is intentionally concrete: PASS or FAIL.
Application fields
Designed for regulated workflows where decisions must remain verifiable.
Medical AI
Triage, risk assessment and human approval boundaries.
Legal & compliance
Reconstructable decision evidence for regulated workflows.
Finance
Auditable policy decisions and transaction-state verification.
Public-sector AI
Traceable AI systems for accountable administrative decision support.
Discuss a BFH / Innosuisse project setup.
DigiEmu can start as one applied research workflow: one decision boundary, one verification prototype, one evaluation report and one audit-ready evidence bundle.
contact@digiemu.ch