RatedWithAI

RatedWithAI

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AI Legal & ComplianceAugust 2, 2026

Selling AI Features to a Federal Agency: What Procurement Will Actually Ask

Public-sector revenue is one of the few segments where an SMB software company can win seven figures without an outbound team. The catch is that federal AI policy is written for agencies, and agencies discharge it by handing the questions to you — usually mid-cycle, usually with a deadline, and usually about documentation you never produced.

Flow-down
Agency obligations arrive as contract clauses and questionnaires, not as regulations
508
Accessibility of the AI interface is a hard gate, not a nice-to-have
Subprocessors
Whose model serves the inference is a boundary question you must answer

The Structural Point Vendors Miss

Federal AI governance is built around agency accountability. Agencies name a responsible official, inventory the AI they use, apply heightened practices where a system affects rights or safety, and publish parts of that record. None of those duties are addressed to you as a vendor.

But an agency cannot document a system it does not build. Every one of those obligations converts into a request for information directed at whoever supplies the AI— and that is where a commercial SaaS company discovers it has no evaluation record, no described intended-use boundary, and no answer to "how does an affected person appeal this output?"

What Shows Up in the Questionnaire

Intended use and known limitations

A plain description of what the system is for, what it is not for, and where performance is known to degrade. Marketing copy will not survive this question, and an answer that claims no limitations reads as an unexamined system.

Evaluation and testing evidence

What you tested against, on what data, with what result, and when. For anything touching eligibility, benefits, enforcement, or employment, expect questions about performance differences across affected groups.

Human review and contestability

Whether a person can review, override, and explain an output, and what the affected individual's path to challenge it looks like. This is the requirement most commercial products have simply never built a surface for.

Data handling and training use

Whether agency data leaves the boundary, whether it is used for training or fine-tuning, retention periods, and which subprocessors touch it. 'We don't train on customer data' needs to be a contractual commitment, not a webpage.

Supply chain transparency

Which foundation models, hosted APIs, and open-source components are in the stack. Software bills of materials are increasingly expected, and the model layer is now part of the answer rather than an exception to it.

Accessibility conformance

A current accessibility conformance report covering the AI interface specifically — streaming output, chat transcripts, agent status, and generated media alternatives, not just the legacy parts of the product.

Incident and change notification

How you notify the agency when a model version changes, when behaviour materially shifts, or when a safety incident occurs. Silent model swaps are a contract-administration problem in this segment.

The Accessibility Gate Is the One That Bites

Section 508 has been enforced against ICT procurement for a long time, and the conformance-report request is routine. What is new is that the AI surface is the least accessible part of a modern product: output that streams token by token without announcing completion, chat panes that trap keyboard focus, generated charts and images with no text alternative, and agent status that changes visually with no programmatic notification. A vendor with a clean legacy conformance report and an inaccessible AI pane fails the same review. This is fixable engineering work, but it takes a cycle you will not have once the solicitation is live.

Why This Is Worth the Trouble for a Small Vendor

  • The documentation is reusable. The same evaluation record, model card, and data-handling commitment answer enterprise security reviews and EU-facing obligations. You are producing one artefact for three buyers.
  • It is a moat, not a tax. Most competitors your size cannot answer the questionnaire, which is precisely why the segment stays winnable for whoever does the work first.
  • State and local follow federal. State procurement offices routinely borrow federal AI questionnaire language, multiplying the return on the same package.
  • Contract vehicles compound. Once you are on a schedule with the documentation in place, additional agency deals do not restart the review from zero.

Preparation Checklist

Build the package before the solicitation, not during it. Nothing here requires a government contract to start.

Write a model card per AI feature: purpose, inputs, limitations, evaluation, human-review designEssential
Get a current accessibility conformance report that explicitly covers the AI interfaceEssential
Document your subprocessor chain down to the inference provider and hosting regionEssential
Make the no-training-on-customer-data commitment contractual, with the exceptions statedContract
Build and document an override and appeal path for consequential outputsProduct
Produce an SBOM that includes model and ML dependencies, not just application packagesSupply chain
Define a model-change notification commitment with a notice periodContract
Determine early whether the deal needs a cloud authorization, and scope the boundary honestlyArchitecture
Keep evaluation results versioned and dated — undated claims are treated as unsupportedEvidence

Accessibility is where public-sector AI deals stall

Before you request a conformance report, find out what an evaluator will find. RatedWithAI scans your product and marketing surfaces for the issues that turn into procurement findings. Start with a free scan.

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Frequently Asked Questions

We sell through a reseller on a schedule. Do these questions still reach us?

Yes. The reseller cannot answer questions about your architecture, evaluation, or data handling, so the questionnaire flows through to you with the reseller's deadline attached. The practical difference is that you get less time and less direct access to the agency to clarify.

Does adding an AI feature to an existing federal contract trigger review?

Frequently, yes. If the feature changes what data is processed, introduces a new subprocessor, or moves the system into a category the agency has to inventory, expect a modification and a fresh review. Shipping it silently as a product update is the version of this that goes badly.

What counts as a rights- or safety-impacting use?

Broadly, systems whose output serves as a principal basis for a decision affecting a person's civil rights, benefits, access to services, employment, or physical safety. The categorisation is the agency's to make, but a vendor whose product plausibly lands there should build the human-review and contestability surfaces before being asked.

Can we just say a human reviews everything?

Only if it is true and demonstrable. Reviewers who approve nearly everything under time pressure are a well-known failure pattern, and sophisticated evaluators ask for override rates. Claiming meaningful human review you cannot evidence is worse than describing an automated process accurately.

How much of this applies to state and local contracts?

A growing amount, because state procurement offices copy federal questionnaire language and add their own state privacy and accessibility requirements. If you build the federal package, most state responses become an editing exercise rather than a new project.

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