Built for the work that cannot fail.
Mission-critical organizations across regulated industries deploy the Citrate substrate on their own hardware, behind their own perimeter, with their own keys.
Defense & aerospace
Procurement, provenance, and audit substrate for primes and the tier-N supply chain.
Defense primes operate AI workloads that cannot leave a controlled perimeter. Tier-N suppliers face cybersecurity requirements that exceed what most can build in-house. Citrate provides the on-premise substrate that handles both.
Procurement primitives ... supplier qualification, part provenance, MOQ variance tracking, classification gating, cross-organization transfer with cryptographic provenance, audit-bundle export ... deploy unchanged for any prime managing a regulated supply chain.
Our federated learning daemon lets multiple programs aggregate insights without sharing raw training data. The cryptographic audit substrate produces evidence packages 3PAO-ready out of the box.
- ●Run inference on-premise with cryptographically verifiable provenance
- ●Train fine-tunes across programs with federated learning
- ●Export audit bundles in 3PAO / DCAA / DCMA-ready format
- ◐CMMC Level 2 self-assessment (T+12 months)
- ◐FedRAMP Moderate ATO (sponsor-dependent timeline)
Manufacturing
Federated learning for predictive maintenance. Supply-chain audit. On-premise AI that respects your trade secrets.
Manufacturers face a hard trade-off: pool data across plants to train better models, or protect process IP from disclosure. Federated learning solves the math; the substrate solves the trust.
Citrate's deterministic federated-learning daemon aggregates updates across plants without ever exposing raw data, and proves correctness cryptographically.
For supply-chain integrity, the same procurement primitives that serve defense primes adapt to automotive recalls, semiconductor export control, and FDA-regulated production.
- ●Predictive maintenance across plants without sharing process parameters
- ●Supply-chain provenance for recall and counterfeit-detection workflows
- ●Audit-ready records for regulator filings (NHTSA, FDA, FAA, others)
Healthcare & pharma
Cross-institution learning without exposing patient data. Designed for federated, audit-ready medical AI.
Healthcare AI has a federated-learning problem that no commercial platform has cleanly solved: pool insight across institutions without ever moving patient data, while producing audit evidence that satisfies HIPAA, HITRUST, FDA SaMD, and IRB review.
Citrate's substrate is designed for exactly that constraint. Pharma genomics consortia, rare-disease research networks, and multi-institutional clinical AI pilots all share the same shape: locally-trained models, cryptographically-verifiable aggregation, complete on-chain audit lineage.
Our daemon ships that pattern as production infrastructure.
- ●Rare-disease model training across institutions
- ●Imaging AI fine-tuning without de-identification risk
- ●Federated clinical-decision-support pilots with audit trails
Banking & insurance
Federated fraud detection. SR 26-2-aligned model risk audit. AI governance that survives regulator review.
Federal Reserve SR 26-2 (April 2026, superseding SR 11-7) raised the bar for AI model risk management. The EU AI Act Annex III raised it again for institutions with European exposure. Most banks and insurers face the same question: how do you prove your AI is governed when the AI vendor controls the substrate?
Citrate inverts the assumption. The substrate is yours. The model weights are yours. The audit ledger is yours. Every privileged action by every AI agent ... human-mediated or automated ... produces a cryptographically distinguishable record.
Federated fraud detection across an industry consortium produces the same provenance properties.
- ●Federated fraud / AML model training across an industry consortium
- ●Model risk audit substrate aligned with SR 26-2 and the EU AI Act
- ●AI agent decision logs separating automated from human-mediated actions
Energy & grid
Distributed compute that respects grid constraints. Federated learning for grid resilience. NERC-aligned audit substrate.
The data-center load problem is real. NERC's recent Level 3 Alert on hyperscaler-driven voltage events made it explicit. Citrate's distributed-compute substrate offers an inverse: sub-MW behind-the-meter compute distributed across facilities, with cryptographic load attestation suitable for regulator filings.
For federated learning across utilities ... grid resilience models, demand-response optimization, equipment failure prediction ... the same framework that serves defense and pharma serves utilities, with NERC CIP-aligned audit primitives.
- ●Sub-MW behind-the-meter distributed compute
- ●Federated grid-resilience and demand-response models
- ●NERC CIP-aligned audit primitives
Other industries we serve.
Do not see your industry? Talk to us anyway.
The substrate generalizes. We have written dossiers for nineteen industries and the methodology applies to more. The first conversation reveals fit faster than any page can.