Service
Deploy, monitor, and update mission-critical AI models across air-gapped networks and tactical edge devices with zero data exfiltration risk.
Traditional cloud-based MLOps fail in secure defense environments. We engineer hardened, sovereign pipelines that meet the strictest compliance mandates for air-gapped networks, tactical edge hardware, and classified data.
Our secure orchestration platforms provide strict version control, one-click rollback capabilities, and full compliance auditing for every model update, ensuring operational integrity under the most stringent governance.
NIST AI RMF, ISO/IEC 42001, and specific defense directives like CMMC from the ground up, with immutable audit logs for all model lifecycle events.This foundational capability enables other critical services, such as Secure Federated Learning for Defense and Secure Edge AI for Deployed Units. For enterprises operating under sovereign data mandates, explore our Sovereign AI Infrastructure Development services.
Our secure MLOps pipelines deliver deterministic, auditable outcomes for deploying and managing AI across classified networks and tactical edge devices. We engineer for compliance, resilience, and operational tempo.
End-to-end MLOps pipelines engineered within accredited, air-gapped environments, ensuring model deployment complies with NIST 800-53, ICD 503, and other stringent defense standards. We implement hardware-based trusted execution and cryptographic model signing.
Immutable model registries with cryptographically verifiable lineage for every artifact—training data, code, parameters, and performance metrics. Achieve one-click rollback to any previous model state to ensure operational continuity and auditability.
Unified orchestration platform managing AI model lifecycles across secure cloud, on-premise data centers, and ruggedized edge hardware. Built-in resilience for Disconnected, Intermittent, and Low-bandwidth (DIL) environments critical for tactical operations.
Automated policy-as-code enforcement and real-time compliance dashboards. Continuously monitor for configuration drift, unauthorized changes, and generate audit-ready reports for Authorizing Officials (AOs), reducing accreditation burden.
Continuous monitoring of model accuracy, latency, and resource consumption in production. Advanced drift detection triggers automated alerts and can initiate secure retraining pipelines before operational effectiveness degrades.
Orchestrate privacy-preserving model training across distributed intelligence units or allied networks without centralizing raw data. We engineer parameter exchange protocols that maintain strict data sovereignty mandates. Learn more about our approach to Federated Learning Systems Engineering.
Our phased methodology ensures a controlled, auditable rollout of secure AI orchestration, minimizing risk and maximizing operational readiness at each stage.
| Phase | Key Activities | Duration | Deliverables | Security Gates |
|---|---|---|---|---|
Phase 1: Security & Compliance Assessment | Threat modeling, policy review, infrastructure audit | 2-3 weeks | Risk Assessment Report, Compliance Gap Analysis | ATO (Authority to Operate) prerequisites met |
Phase 2: Secure Pipeline Architecture | Design air-gapped MLOps, implement zero-trust access, configure secure enclaves | 3-4 weeks | Approved System Design Document, Secure CI/CD Pipeline | All designs meet NIST SP 800-171 / CMMC Level 3 standards |
Phase 3: Hardened Model Deployment | Containerize models with FIPS-validated encryption, deploy to accredited cloud/edge | 2-3 weeks | Deployed Models in Staging, Performance & Security Benchmarks | Successfully passes adversarial red teaming test |
Phase 4: Orchestration & Monitoring Go-Live | Activate full orchestration platform, enable real-time monitoring & drift detection | 1-2 weeks | Operational Orchestration Platform, Live Monitoring Dashboard | 99.9% uptime SLA validated, full audit trail active |
Phase 5: Sustained Operations & Evolution | Ongoing model updates, security patching, performance optimization | Ongoing | Monthly Ops Reports, Incident Response Playbooks, Model Retraining Pipelines | Continuous compliance with evolving standards (e.g., MITRE ATLAS) |
Total Time to Full Operational Capability (FOC) | 8-12 weeks | Fully operational, secure, and compliant AI deployment platform | All security gates passed, full operational responsibility transferred |
Deploy and orchestrate AI models across air-gapped networks, tactical edge devices, and secure enclaves with full compliance, auditability, and resilience against adversarial threats. Our engineering ensures your models operate with integrity where failure is not an option.
Engineer secure MLOps pipelines for deploying and updating models within accredited, air-gapped environments. We implement strict version control, rollback capabilities, and full audit trails to meet DoD and IC compliance standards without external connectivity.
Deploy optimized, small-footprint AI models on ruggedized edge hardware for real-time intelligence processing in disconnected, intermittent, and low-bandwidth (DIL) environments. Our orchestration platforms manage model updates, health, and performance across distributed units.
Architect secure data diodes and one-way transfer systems to enable controlled AI model and intelligence sharing between networks of different classification levels (e.g., NIPRNet to SIPRNet), ensuring data sovereignty and preventing exfiltration.
Automate the generation of compliance evidence for security controls (NIST 800-53, CNSSI 1253), algorithmic accountability, and operational readiness. Our systems produce detailed logs and reports for internal security reviews and external accreditation bodies.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
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Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Critical questions and specific answers for deploying secure, scalable AI models and orchestration platforms in classified and tactical edge environments.
For a standard secure MLOps pipeline deployment, we deliver a production-ready environment in 4-6 weeks. This includes hardened infrastructure provisioning, model containerization, secure CI/CD integration, and initial model deployment. Complex multi-domain or air-gapped deployments with custom sensor integration typically extend to 8-12 weeks. We provide a detailed, phased project plan during the initial technical assessment.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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