Built for regulated environments
Every industry that uses AI in high-stakes decisions needs cryptographic proof that the system works as claimed. We build that proof.
Defense / Government
Section 1513 of the FY2026 NDAA directs the Department of Defense to develop an AI/ML security framework and incorporate it into both DFARS and CMMC. The DoD status update to Congress is due June 16, 2026.
Small-to-mid defense contractors need to prove their AI usage is compliant with incoming CMMC AI extensions. Primes need subcontractors who can generate verification artifacts on demand. Nobody has productized this for the Defense Industrial Base mid-market.
The framework must address workforce risks, supply chain risks, adversarial tampering, and continuous security monitoring. Contractors without cryptographic verification evidence will face assessment failures and contract disqualification.
How we solve it
The Provenance Layer (IRONROOT) generates AI bills of materials with traceable, auditable rationales: the exact characteristics NIST defined for trustworthy AI in critical infrastructure. Every AI decision gets a tamper-evident, write-once audit record with 140 tests across 5 capability layers validating integrity.
Adversarial Verification (AASMS) provides documented adversarial robustness through Red and Blue team stress testing: 6 evolution cycles, 24 proposals verified, 0 errors. This maps directly to the NIST profile's "adversarial tampering" coverage and Section 1513's adversarial risk requirements.
Contradiction Detection (ABES) delivers verifiable performance guarantees with 822 tests, a 1,000-prompt evaluation pipeline, and a 0% hallucination rate in controlled runs. Decision Auditing (RLFusion) provides full trace logging across 6 test suites with 3,000+ iterations at a 100% pass rate.
All verification runs on local hardware with no cloud dependency, satisfying data sovereignty requirements. RTX 5070 with Llama 3.1 8B via Ollama. No data leaves your infrastructure.
Healthcare
The FDA requires proof that the specific approved model ran on specific patient data. Most AI vendors cannot provide this. When a diagnostic AI recommends a treatment path, there is no cryptographic record linking the model version, the input data, and the output decision.
HIPAA compliance adds another layer: patient data cannot leave the facility for cloud-based inference. Vendors relying on cloud APIs create compliance exposure with every query.
How we solve it
The Provenance Layer (IRONROOT) tracks model version to input mapping with write-once cryptographic records. Every inference gets a tamper-evident audit trail linking the exact model, the exact input, and the exact output. 140 tests validate chain integrity across 5 capability layers.
Contradiction Detection (ABES) flags uncertainty before it reaches a clinician. The 14-phase agent pipeline scores entropy and surfaces conflicting evidence rather than presenting false confidence. 822 tests and a 1,000-prompt evaluation confirm 0% hallucination rate in controlled runs.
All processing runs locally. No patient data leaves your infrastructure. HIPAA-safe by architecture, not policy.
Finance
Regulators demand explainable AI decisions with complete audit trails. Fraud detection algorithms, credit scoring models, and trading systems all require cryptographically verifiable decision chains. A regulator asking "why did the system make this decision?" needs a traceable answer, not a probability distribution.
Current AI platforms produce outputs with no record of reasoning. When enforcement actions arrive, institutions cannot reconstruct the decision path.
How we solve it
Decision Auditing (RLFusion) provides full trace logging for every query, from input through retrieval paths to verified output. 6 test suites with 3,000+ iterations at a 100% pass rate confirm end-to-end reliability at approximately 10-second latency.
Adversarial Verification (AASMS) stress-tests models in Docker-isolated sandboxes before deployment: 6 evolution cycles, 24 proposals verified, 0 errors. Only verified systems reach production.
The Provenance Layer (IRONROOT) creates write-once decision records. Every trade recommendation, credit decision, and fraud alert includes a cryptographic proof chain that regulators can independently verify.
Legal
AI-generated document analysis and evidence processing requires chain of custody. Courts require proof of reasoning, not just results. When an AI system reviews thousands of documents for relevance in discovery, opposing counsel will challenge the methodology and demand verifiable evidence of how decisions were made.
Current AI tools produce outputs with no provenance. There is no record of which model version processed which document or what confidence level informed the classification.
How we solve it
Contradiction Detection (ABES) provides full decision traces with confidence levels and contradiction resolution records. The 14-phase pipeline surfaces conflicting evidence rather than silently resolving it, producing auditable reasoning chains that withstand legal scrutiny.
Decision Auditing (RLFusion) logs every query path from input to output with source attribution. 3,000+ test iterations across 6 suites at a 100% pass rate validate trace completeness.
The Provenance Layer (IRONROOT) maintains cryptographic chain of custody for every document-level AI decision. Verifiable end-to-end, from intake to classification to output.