I Gave ChatGPT and Govafy the Same Government RFP. Here's What Happened.

ChatGPT vs Govafy - who wins?

By Abraham Xiong | Founder, Govafy | March 2026

There's a question I keep hearing from government contractors: "Can I just use ChatGPT to write my proposals?"

It's a fair question. ChatGPT is powerful. It's fast. It's everywhere. Over 90,000 government employees across 3,500 agencies are already using it for daily tasks. And if you've ever pasted an RFP into ChatGPT and gotten something back that looked halfway decent, you probably thought — why would I need anything else?

So, I decided to put it to the test. Not with a hypothetical. With a real Sources Sought notice, real company data, and a side-by-side comparison that would show exactly where general-purpose AI stops and purpose-built GovCon AI starts.

I recorded the entire process. You can watch the full video breakdown here (https://www.youtube.com/watch?v=g69dpCz0qvE). But if you want the detailed analysis, keep reading.

The Test

I pulled a live Sources Sought notice from SAM.gov — Website Design, Development, and Maintenance Support for the Office on Women's Health (OWH) at the U.S. Department of Health and Human Services.

I created a fictional company — Catalyst Federal Partners, LLC — with realistic credentials: SBA 8(a) certified, Woman-Owned Small Business, HUBZone, ISO 9001:2015. I gave it three past performance references with specific contract numbers, dollar values, and measurable outcomes.

Then I fed the exact same inputs into two platforms: ChatGPT and Govafy. Same notice. Same company. Same past performance. Same data. The only variable was the AI.

What came back was not a close call.

What ChatGPT Produced

ChatGPT delivered a clean, 9-page document organized into ten sections: company overview, understanding of the OWH mission, core capabilities (broken into six subsections), past performance, digital communications, contract vehicles, capability to perform as prime, NAICS feedback, point of contact, and a conclusion.

On the surface, it looks professional. The formatting is clean. The language is polished. If you've never written a Sources Sought response before, you'd probably feel good about submitting this.

But here's the problem — it reads like a capability statement, not a Sources Sought response. And in government contracting, that distinction matters more than most people realize.

A Sources Sought notice is the government conducting market research. The contracting officer is asking a very specific question: "Are there qualified companies that can do this work, and should we set this aside for small businesses?" That's it. The evaluator isn't scoring your technical approach or comparing you against competitors. They're deciding whether capable vendors exist in the marketplace.

ChatGPT never directly answers that question. It starts with a company overview and works its way through capabilities and past performance — the way a marketing brochure would. The evaluator has to read the entire document and infer whether this company can perform the work. There's no direct declaration. No compliance mapping. No structured evidence linking capabilities to the specific requirements in the notice.

It's not wrong. It's just not built for the task.

What Govafy Produced

Govafy came back with a document that looks like it was written by a capture manager who has submitted hundreds of these.

It opens with a compliance-formatted cover page — solicitation number, agency, submission details, vendor identification, point of contact, certifications, and a proprietary notice. Before the evaluator reads a single word of substance, they already know this company is organized and understands the process.

The Executive Summary begins with what I call a "Quick Answer" — a bold, unambiguous YES followed by the company's UEI, CAGE code, socioeconomic status, and a one-sentence declaration that Catalyst is a qualified, interested prime capable of performing the work. In the first three seconds of reading, the evaluator knows everything they need to know to make their initial determination.

Then it goes deeper. The executive summary provides evidence-based capability fit — not generic claims, but specific contracts with specific metrics tied directly to the notice requirements. NIA engagement produced a 67% user-engagement increase. FDA engagement achieved 100% WCAG 2.1 AA compliance on DHS Trusted Tester. These aren't just numbers dropped into a document. They're mapped to the exact capabilities OWH is looking for.

From there, the response moves through a Vendor Profile, a Socioeconomic Status section with set-aside eligibility analysis, a detailed Capability Statement with requirement-by-requirement mapping, Past Performance narratives that connect outcomes to notice requirements, a phased Technical Approach, a Staffing and Capacity plan, Acquisition Feedback, Questions and Clarifications, and a Compliance Checklist.

That's not a capability statement with a few extra paragraphs. That's a fundamentally different document.

The Five Sections ChatGPT Didn't Even Attempt

This is where the gap becomes a canyon.

Technical Approach. Govafy included a phased methodology: Discovery and Governance in Weeks 0–2, Design and UX in Weeks 2–6, then ongoing development sprints. It specified tools — axe-core and Pa11y for automated accessibility scans, Lighthouse for performance, GitHub Actions for CI/CD, Terraform for infrastructure-as-code, Datadog and New Relic for monitoring. It named deliverables for each phase. ChatGPT's response included no technical approach whatsoever.

Staffing and Capacity. Govafy listed 22 personnel by role — Program Manager, UX designers, front-end developers, back-end engineers, accessibility specialists, DevSecOps engineers, content strategists, analytics specialists — with specific availability dates (Program Manager available Day 1, core team in place by Day 14). It disclosed that 9 staff hold active federal background investigations. It included surge capacity: 40 professionals available within 2 weeks, 120 within 6–8 weeks through established subcontractor networks. ChatGPT mentioned no staffing details at all.

Acquisition Feedback. This is the section that signals you understand how government procurement actually works. Govafy recommended a Multiple-Award IDIQ contract structure. It suggested Firm-Fixed-Price for discrete development projects and Time-and-Materials for continuous operations with not-to-exceed ceilings. It recommended a partial small business set-aside and identified five specific acquisition risks — unclear UWP integration boundaries, accessibility compliance ambiguity, security/hosting uncertainty, single-award continuity risk, and transition knowledge gaps — each with concrete mitigations. ChatGPT offered one paragraph suggesting alternative NAICS codes.

Questions and Clarifications. Govafy generated 11 substantive questions organized across scope, technical, security, performance, data, and staffing categories. Questions like: "What CMS vendor and version is currently in use?" and "Will the contractor perform remediation for identified vulnerabilities or only coordinate with OASH OCIO on security incidents?" These aren't boilerplate. They're the kind of questions that tell a contracting officer you've actually read the notice and understand the operational complexity. ChatGPT asked zero questions.

Compliance Checklist. Govafy included a structured matrix tracking compliance with every requirement identified in the notice. ChatGPT did not include one.

Why This Matters

Put yourself in the shoes of a contracting officer. You've published a Sources Sought notice. You're getting 20, maybe 30 responses. You need to determine market capability and make a set-aside decision. You're reading these in batches between other tasks.

One response starts with a company overview and reads like a generic marketing brochure that could apply to any website project at any agency. You skim it in two minutes, check a box, and move on.

Another response opens with a clear YES, provides mapped evidence, includes a technical approach, names 22 people ready to mobilize, gives you acquisition structure recommendations, asks smart questions, and hands you a compliance checklist. This is the response that gets a phone call. This is the response that gets you on the vendor list.

The difference isn't cosmetic. It's strategic. And it comes down to whether the AI writing your response understands the document type, the evaluator's mindset, and the procurement context — or whether it's just generating well-formatted text.

The Scorecard

After comparing both responses across five categories, here's where they landed:

Structure and Completeness — ChatGPT produced a standard capability statement format. Govafy produced a fully structured Sources Sought response with cover page, compliance formatting, and every expected section. Clear advantage: Govafy.

Answering the Core Question — ChatGPT required the evaluator to infer capability from a narrative. Govafy opened with an explicit YES and evidence-based proof. Clear advantage: Govafy.

Past Performance Presentation — Both referenced the same contracts and metrics. ChatGPT listed them. Govafy mapped them to notice requirements and explained their relevance. Advantage: Govafy.

Technical Depth — ChatGPT included no technical approach, no staffing, and no acquisition feedback. Govafy included all three with specificity. Decisive advantage: Govafy.

Evaluator Readiness — ChatGPT's response would be filed. Govafy's response would generate a follow-up. Decisive advantage: Govafy.

What This Doesn't Mean

I want to be clear about something: this is not a hit piece on ChatGPT. I use ChatGPT every single day. It's an extraordinary tool for brainstorming, drafting, research, coding, and a hundred other tasks. OpenAI has built something genuinely impressive, and it belongs in every professional's toolkit. The main issue here is that ChatGPT is a general AI writing tool.

But government proposal writing is a specialized discipline. It has its own document types, its own evaluation criteria, its own compliance structures, and its own reader psychology. A Sources Sought response is not an essay. An RFP response is not a report. These documents have specific purposes, specific audiences, and specific standards — and an AI that doesn't encode those standards will produce output that looks right but misses the mark.

That's the gap Govafy was built to fill. Not to replace general-purpose AI, but to do the one thing it can't: write like a capture professional who understands how government contractors win work.

Try It Yourself

If you're a government contractor and you want to see what Govafy produces for your next Sources Sought, RFI, or proposal response, try it free at govafy.com. https://govafy.com 

You can also download both responses side by side and judge for yourself.

And if you want to watch me walk through every section of both documents in real time, the full video is here.

Abraham Xiong spent 15 years leading the Government Contractors Association, supporting over 16,000 organizations and helping drive more than $2 billion in contract awards. He is the founder of Govafy, an AI-native platform built specifically for government contractors.