Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Engineer AI hosting environments that meet stringent U.S. Federal Risk and Authorization Management Program (FedRAMP) security controls.
Deploy AI with authorized cloud services for government agencies and contractors. Our service delivers a fully compliant environment—from initial security control mapping to final authorization support—enabling you to focus on mission-critical AI applications.
We architect the secure foundation so you can deploy AI with confidence, meeting federal mandates without sacrificing innovation speed.
Terraform and Ansible with auditable, repeatable configurations for AWS GovCloud, Azure Government, or on-premises stacks.Security Package templates and expertise to shorten your Authority to Operate (ATO) timeline by 40%.This service is part of our broader Sovereign AI Infrastructure Development pillar, which also includes Air-Gapped AI System Deployment for the highest security assurance and EU AI Act Compliant AI Development for European mandates.
Achieve mission-critical AI deployment with a platform engineered to meet the highest U.S. federal security standards, delivering tangible operational and strategic advantages.
Leverage our pre-built, continuously audited infrastructure to reduce your FedRAMP authorization timeline from 12-18 months to under 6 months. Our platform includes pre-configured security controls, documented evidence packages, and integration with accredited FedRAMP 3PAOs.
Deploy AI with a 99.9% uptime SLA backed by geographically redundant, U.S.-based data centers. Our infrastructure ensures high availability for critical applications, with automated failover and continuous monitoring to meet Federal Continuity of Operations (COOP) requirements.
Transition from unpredictable cloud spend to a fixed-cost, FedRAMP-authorized environment. Our platform includes built-in FinOps tools for AI workload optimization, eliminating the cost and complexity of securing and monitoring a DIY compliant cloud.
Our structured, multi-phase approach to building FedRAMP-compliant AI infrastructure reduces technical and compliance risk, accelerates the authorization timeline, and ensures continuous value delivery.
| Phase & Deliverables | Timeline | Key Outcomes | Authorization Progress |
|---|---|---|---|
Phase 1: Security Control Gap Analysis & Architecture Blueprint | 2-3 weeks | Comprehensive FedRAMP control mapping, technical architecture design, and prioritized remediation roadmap. | Foundational documentation for System Security Plan (SSP). |
Phase 2: Core Infrastructure & Technical Control Implementation | 4-6 weeks | Deployment of secure, air-gapped compute environment; implementation of logging, monitoring, and encryption controls per NIST 800-53. | Ready for 3PAO pre-assessment; 60% of technical controls validated. |
Phase 3: Continuous Monitoring & Policy Automation | Ongoing | Operationalization of continuous monitoring tools, automated compliance checks, and security incident response playbooks. | Full operational capability (FOC); supports ongoing assessment for Authority to Operate (ATO). |
Total Time to Operational Readiness | 6-9 weeks | Fully functional, compliant AI hosting environment ready for model deployment and security assessment. | Accelerates path to Provisional Authorization to Operate (P-ATO) by 40-60%. |
Compared to In-House Build | 6-12+ months | Eliminates unguided control implementation, reduces rework, and provides expert-led compliance narrative. | High risk of failed assessment or major findings without experienced FedRAMP partner. |
Our FedRAMP-compliant infrastructure is engineered to meet the stringent security and operational demands of U.S. federal agencies, defense contractors, and intelligence communities, enabling secure AI deployment at scale.
We engineer and operate AI environments that meet all FedRAMP security controls. Our systems are designed for authorization, with continuous monitoring and audit-ready documentation to support your agency's ATO process.
For the most sensitive workloads, we design and deploy fully isolated AI training and inference clusters with no external network connectivity, preventing data exfiltration and ensuring the highest security assurance. Learn more about our Air-Gapped AI System Deployment methodology.
Guarantee that all training data, model weights, and inference outputs are processed and stored exclusively within U.S. borders. We implement technical controls and provable audit trails for full data lineage and residency assurance, aligning with mandates for Sovereign AI Data Residency Assurance.
We provision and manage dedicated AI accelerators (GPUs, NPUs) and compute clusters that are physically reserved for your entity. This ensures performance isolation, prevents resource contention, and mitigates supply chain risks through vetted procurement.
Our infrastructure includes AI-native security monitoring that goes beyond traditional IT. We implement frameworks like MITRE ATLAS to detect and respond to novel AI threats, including model manipulation and data poisoning, as part of our AI Red Teaming and Adversarial Defense services.
Operate a complete, sovereign machine learning lifecycle within your controlled environment. Our platform enables secure model development, versioning, CI/CD, and monitoring without reliance on external, non-compliant SaaS tools, ensuring end-to-end governance.
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.
Get clear, specific answers to the most common questions about deploying AI in environments that meet U.S. federal security standards.
For a standard deployment, the typical timeline is 2-4 weeks from kickoff to initial operational capability. This includes environment provisioning, security control implementation, and initial model deployment. Complex integrations with legacy government systems can extend this to 6-8 weeks. Our fixed-scope methodology ensures predictable delivery.
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.
The first call is a practical review of your use case and the right next step.
