Cloud AI Solutions for Government
Run generative AI and machine‑learning securely on a government‑ready cloud. Evaluate multiple models side‑by‑side, right‑size GPU compute, enforce role‑based access, and document controls for ATO—fully managed by a U.S. team in U.S. datacenters.
Who This Is For
- Federal, state, and local agencies that need to test and refine foundation models.
- Prime and sub‑contractors collaborating with agencies under strict data controls.
- Program offices, CISOs, and ATO owners seeking a practical path from pilot to production.
Outcomes You Can Expect
- Faster evaluations: Stand up a secure AI lab and compare models in days.
- Operational readiness: Promote the best approach to a production enclave with auditable controls.
- Right‑sized performance: GPU tiers for training and inference without long‑term lock‑in.
- Assurance & support: 24×7 managed operations with documented responsibilities and SLAs.
Get a tailored quote with environment sizing, GPU options, and support tier
What You Can Do Here
1) Evaluate and Select Models
- Run multiple foundation models side‑by‑side (open‑source or licensed).
- Ground models with your documents via retrieval and vector search.
- Capture accuracy, latency, and cost metrics to inform selection.
2) Train, Fine‑Tune, and Optimize
- Launch training or fine‑tuning jobs in a dedicated enclave.
- Use managed artifacts (weights, embeddings, prompts) with versioning.
- Schedule batch jobs; autoscale workers for spikes.
3) Serve and Monitor in Production
- Publish inference endpoints with rate limits and audit trails.
- Log prompts/outputs (redacted as needed) for quality and compliance.
- Continuous monitoring with alerting, ticketing, and monthly reports.
Platform Capabilities
The AI Fusion Lab (Pilot Safely, Decide Confidently)
A neutral, secure environment to evaluate approaches before you commit.
How it works:
- Intake: Load representative, unclassified (or approved) datasets.
- Compare: Run candidate models with identical prompts and guardrails.
- Ground: Add retrieval over your corpus; measure factuality and drift.
- Decide: Select the best model/architecture based on accuracy, latency, and cost.
- Promote: Migrate artifacts into a production enclave with stricter controls.
- Deliverables: method of evaluation, metrics dashboard, decision memo, and a ready‑to‑authorize deployment plan.
Reference Architecture (at a glance)
- Tenant Boundary: Agency → Program → Project enclaves
- Data Plane: Object store, vector database, feature store
- Model Plane: Registry for weights, embeddings, prompts, and policies
- Compute Plane: Training nodes, inference autoscaling group, batch workers
- Network Plane: Private endpoints, WAF, API gateways, service mesh
- Observability: Logs, metrics, traces, prompt/response archives
- Security: IAM, KMS/HSM, vulnerability scanning, CIS hardening, backup/DR
(Ask us for a one‑page diagram you can attach to your A&A package.)
Pricing & Procurement
- Flexible models: fixed, reserved, or consumption‑based for GPUs and storage.
- Include support tier and environment size in your RFP/RFQ for accurate quotes.
- Available for agency and contractor procurements; teaming-friendly.
Provisioning Speed
- Typical pilots begin within days after security prerequisites are met.
- Production environments follow a standard build book with repeatable timelines (publishable upon request).
Ready to scope your AI pilot or production deployment?
What is a secure AI cloud for U.S. government use?
A managed environment that enforces RBAC, network isolation, encryption, audit logging, and documented controls mapped to NIST SP 800‑53 Rev. 5—operated in U.S. datacenters by U.S. personnel.
Can we test different foundation models before choosing one?
Yes. Use the AI Fusion Lab to compare open‑source and proprietary models with identical prompts, datasets, and guardrails.
Do you support both open‑source and proprietary models in one tenant?
Yes. We host OSS and licensed models in segregated enclaves with signed artifacts and access policies.
What GPU options are available for training vs. inference?
Training tiers emphasize multi‑GPU and high memory; inference tiers optimize for throughput and cost. Both can scale elastically.
How do you segment teams and contractors securely?
Per‑project enclaves, RBAC, private endpoints, and immutable audit logs keep data and changes traceable and contained.
How do you protect training data, prompts, and outputs?
Redaction, safe‑logging, content filters, and policy‑based sharing guard sensitive inputs/outputs; encryption and access controls protect datasets and artifacts.
How do you prevent prompt injection and data exfiltration?
Guardrail policies, input/output validation, allow‑listed tools, and egress controls on inference endpoints block common injection/exfil paths.
What’s the path from pilot to ATO?
Pilot in the AI Fusion Lab, capture metrics and baselines, then promote to a production enclave with hardened images, formal change control, and a documentation set aligned to your A&A process.
How fast can you provision a secure AI environment?
Pilots can begin in days after prerequisites; production follows a standard build book (timeline shared during scoping).
Do you map controls to NIST AI RMF and OMB M‑24‑10?
Yes. We align artifacts to NIST AI RMF functions and support governance needs under OMB M‑24‑10 (inventory, risk/impact, continuous monitoring).
What SLAs apply to model hosting and inference uptime?
Infrastructure availability SLA with 24×7 monitoring; per‑endpoint rate limits and health checks maintain reliability.
Are your datacenters and support U.S.‑only?
Yes. Workloads run in U.S. facilities and are supported by U.S. citizens.
