FAQ

Large platform deployments are typically built around the vendor's data ontology and forward-deployed teams — you gain sovereignty of infrastructure, but a dependency on the platform. Frontier Foundry deploys into your data model and your governance systems, and your own team can operate it. In one deployment, Skyra produced its first output within 24 hours of go-live. It is built for institutions under the same regulatory and adversarial pressure as the largest agencies, without requiring reference-architecture budgets to match. Every output is a deterministic ensemble result that can be replayed for examiners. We go deeper on the distinction in Sovereign AI's Missing Pieces.

An ensemble AI is a coordinated system of many specialized models — purpose-built for distinct analytical tasks rather than a single general-purpose model attempting all of them. Frontier Foundry's ensemble combines back-propagation neural networks, classical statistical models, and custom generative AI, each trained on a specific signal type, factor regime, or risk category. Outputs are cross-validated across the ensemble before they reach the user, producing deterministic, auditable results suitable for regulatory examination — instead of the variable, opaque outputs of a single general-purpose LLM. Frontier Foundry's core models are proprietary and not transformer-based. They produce the same output for the same inputs, model versions and configuration. Where a product has a natural-language interface, through Limni, a proprietary non-transformer language model interprets the question and drafts text. It does not make the decision, and its output is logged next to the deterministic result. Skybird's detection and tracking use open computer-vision models, which run inside the deployment like any other model.

By removing the analyst-bandwidth bottleneck. A senior analyst can manually track risk signals across maybe a few dozen positions in a day; Kundi's ensemble does it across the full portfolio continuously. At a multi-strategy hedge fund (Skyra), this produced an 85% reduction in portfolio-analysis time, 90% reduction in manual reporting, and a 3× ROI in Year 1. Recommendations are deterministic and audit-traceable, so they hold up under regulatory examination instead of being treated as one analyst's opinion.

Yes — every deployment is bespoke. Our primary verticals are banking (BSA/AML, NYDFS Part 500), asset management, government (designed for air-gapped and classified environments, SCIF-deployable), and healthcare (HIPAA-aligned). Models are trained on your historical data, inside your environment, against the specific signal types and factor regimes your workflows depend on. We do not ship a single generic product across them.

Our founder, Sultan Meghji, served as Chief Innovation Officer at the U.S. FDIC, reporting directly to the Chairman; previously co-founded and ran Neocova. Our CTO, Dr. Roque Martinez, has 30+ years of fintech experience and was previously CTO of SmartStream. Our Managing Director, Reem Abbassi, has 20+ years in financial services running OMS implementations and enterprise data migrations. Across the team: regulator engagement, banking infrastructure, machine-learning engineering for production systems, and on-the-ground experience with the operational realities of regulated AI deployment.

First, our systems run inside your infrastructure, so your data stays on your network, and the models and weights trained on it remain yours. Second, Kundi runs a coordinated ensemble drawn from the COSMIC library of over 1,000 algorithms — back-propagation neural networks, classical statistical models, and custom generative AI — rather than a single general-purpose generative AI; outputs are cross-validated and reproducible, suitable for regulatory examination. Third, our engagements continue through production: we stay with you in production, supporting your team or running the system alongside it; we don't deliver it and leave.

Security controls are part of the architecture: network isolation, policy enforcement and audit logging on every operation. Systems run inside customer-controlled infrastructure with AES-256 encryption at rest, TLS 1.2+ in transit, runtime isolation, and integration with enterprise IAM (Azure AD, Okta, SAML, SSO). Frontier Foundry has completed a SOC 2 Type II examination covering a 2026 examination period. The report and auditor details are available under NDA. Deployments align with HIPAA, BSA/AML, GDPR, and NIST Cybersecurity Framework, with full audit trails tied to user and model version. Because the system runs entirely in your environment, the trained models and their weights remain yours. Full technical detail is on our Security & Compliance page.

Banks, hedge funds, healthcare organizations, and defense, intelligence and regulatory agencies. Engagements are confidential by default. Published case studies — including the Skyra deployment at a multi-strategy hedge fund — are available with explicit client authorization on the Case Studies page; further references are available under NDA after a briefing.

In a briefing, our team will help you determine the most effective AI use cases for your situation, give practical recommendations on how to prepare your infrastructure for integration, and explain how you can get the most out of our solutions.

First output usually comes within days to a few weeks of go-live. At one multi-strategy hedge fund, Skyra produced its first output within 24 hours of go-live. Results from that fund's first year, against the 12 months before deployment: Sharpe ratio up 40%, visible within 3 weeks of launch, measured on monthly returns net of fees; portfolio-analysis time down 85%; manual reporting down 90%; 3× ROI in Year 1, our standard. Past performance is not indicative of future results. Per-engagement timelines are in the case study and discussed during scoping.

Yes. Frontier Foundry's systems can run on-premises, inside your infrastructure. Whether that's a data center, your private cloud (AWS, Azure, GCP), a hybrid configuration, or a fully air-gapped environment, your sensitive data does not leave your network and there are no calls to external AI providers.

Yes. Frontier Foundry supports fully air-gapped deployments with no external network connectivity, including no connection to Frontier Foundry's own infrastructure. Model updates and patches are delivered through customer-controlled platforms. This deployment mode is used by defense, intelligence and regulatory agencies operating in air-gapped environments and by financial institutions with the strictest data residency requirements.

Cloud-based LLMs are excellent general-purpose tools and they're the right choice for many workflows. However, when your data can't leave your infrastructure, when outputs need to be deterministic and auditable for regulatory examination, or when you need to integrate AI into institutional governance, IAM, and audit systems, they cannot offer the security or specialization needed. Frontier Foundry's solutions are designed to work within the established workflows and compliance guidelines of regulated industries whose needs align with these specific requirements.

Frontier Foundry has completed a SOC 2 Type II examination covering a 2026 examination period. The report and auditor details are available under NDA. Deployments are designed to support HIPAA, BSA/AML, GDPR and the NIST Cybersecurity Framework.

SR 26-2 is the revised U.S. banking agency guidance on model risk management, issued in April 2026, which replaced SR 11-7 and SR 21-8, the 2021 interagency statement on model risk management for BSA/AML systems. It is addressed mainly to banking organizations with over $30 billion in assets. Its definition of a model excludes deterministic rule-based processes with no statistical, economic, or financial theory behind them. Generative and agentic AI are outside its scope, though a bank’s own governance should still cover them.

Built for model risk programs under the agencies' revised model risk guidance (SR 26-2, April 2026, which replaced SR 11-7 and SR 21-8). Kundi's deterministic decision path, versioned models and a full audit trail provide the evidence validators need for conceptual soundness, ongoing monitoring, and outcomes analysis. A validation pack is available on request.

No. Frontier Foundry's core models are proprietary and not transformer-based. They produce the same output for the same inputs, model versions and configuration. Where a product has a natural-language interface, through Limni, a proprietary non-transformer language model interprets the question and drafts text. It does not make the decision, and its output is logged next to the deterministic result. Skybird's detection and tracking use open computer-vision models, which run inside the deployment like any other model.

Our deployments map to the NIST AI RMF functions — Govern, Map, Measure, and Manage — with a control-by-control crosswalk available on request.

Designed to support high-risk-system obligations under the EU AI Act — record-keeping, transparency, and human oversight — ahead of the December 2, 2027 application date for Annex III systems.

Post-quantum cryptography protects data, keys, and connections across all of our platforms, using the FIPS-approved algorithms ML-KEM, ML-DSA, and SLH-DSA (FIPS 203, 204, and 205), each verified byte-for-byte against official test vectors.

Our platforms run on-premises or fully air-gapped inside an agency's existing authorization boundary, so they inherit its security controls. Qualified government buyers can request details under appropriate agreements.

COSMIC is the verified library of algorithms and models behind every Frontier Foundry application: over 1,000 algorithms, independently implemented in 7 languages and pinned by a SHA-256 content hash.