---
title: "What Government Buyers Should Require From an AI Vendor"
slug: "what-government-buyers-should-require-from-an-ai-vendor"
author: "ibl.ai"
date: "2026-05-25 09:00:00"
category: "Premium"
topics: "sovereign AI government, FedRAMP AI, NIST 800-53, air-gapped AI federal, public sector AI, ChatGPT Gov alternative"
summary: "Government AI procurement should test for sovereignty, ownership, and control — not just model quality. Here's the checklist agencies should hold every vendor to."
banner: ""
thumbnail: ""
---

Government agencies are under pressure to adopt AI quickly. But public-sector requirements — data sovereignty, auditability, procurement rules, and security controls — make the consumer and SaaS playbook a poor fit.

The agencies that adopt AI well will be the ones that evaluate vendors on the right criteria. Here's the checklist worth holding every AI vendor to.

## 1. Can it run sovereign and air-gapped?

The first test is deployment. Can the platform run on-premise, in GovCloud, and in a [fully air-gapped](/service/air-gapped-ai) environment with no external connectivity?

Many AI products offer "on-premise" that still phones home for model serving or licensing. For classified and IL5 workloads, that's disqualifying. True sovereignty means zero external dependencies after deployment.

## 2. Do you own the code and the data?

Procurement should ask whether the agency receives the source code or merely licenses access. Ownership — via a [full code license](/full-code-license) — is what enables source-level security review, long-term continuity, and freedom from vendor lock-in.

Data must stay inside the agency's perimeter, with every interaction logged for IG investigations and FOIA compliance.

## 3. Does it meet the control frameworks?

NIST 800-53 alignment, FedRAMP pathways, PIV/CAC authentication, and complete audit trails should be table stakes. The question is whether these are properties of the architecture or promises in a contract. Owned, self-hosted systems make them demonstrable.

## 4. Is it model-agnostic?

Agencies shouldn't bet a multi-year program on one vendor's models. A [model-agnostic platform](/product/agentic-os) lets an agency run private open models for sensitive workloads and switch as capabilities and approvals evolve — without re-procuring the platform.

This is a structural advantage over both the consumer "Gov" editions of frontier models and single-model enterprise vendors.

## 5. Who owns and operates the vendor?

For government and defense, the vendor's own profile matters. ibl.ai is **family-owned and operated from New York, NY** — a domestically-owned, independent, long-term partner, not a foreign-owned or venture-controlled company optimizing for its next raise.

That independence and continuity are exactly what multi-year public programs need.

## 6. Will the agency build capability, not dependency?

The best engagements transfer capability. ibl.ai's [forward-deployed engineers](/service/forward-deployed-engineering) deploy the platform in the agency's environment, integrate it with existing systems, and hand operational ownership to agency staff — so the agency owns the system after knowledge transfer.

## The takeaway

Government AI procurement should test for sovereignty, ownership, control, model freedom, and a stable domestic partner — not just model quality. See the [government solution](/solutions/government), the [self-hosted AI](/self-hosted-ai) hub, and the [ChatGPT Gov alternative](/resources/alternatives/chatgpt-gov-alternative) for how ibl.ai meets the checklist.