AI Fundamentals Through a GovCon Lens

Context windows: how many pages of an RFP can AI process

The single most important technical concept for govcon AI use is the context window. This is the amount of text an AI model can process in a single interaction — think of it as the model's working memory.

Here is why this matters: a typical government RFP for a services contract is 150-300 pages. The solicitation document itself might be 50-80 pages, but when you add the SOW/PWS, attachments, CLINs, CDRLs, Section K representations, and referenced documents, you are easily past 200 pages.

Until 2024, most AI models had context windows of 4,000-8,000 tokens — roughly 6-12 pages of text. That was useless for govcon. You could not even fit Section L into the context window, let alone the full RFP.

Current models have context windows of 100,000 to 200,000 tokens. That is approximately 150-300 pages of text. This means you can now feed an AI model the entire RFP — every section, every attachment, every referenced clause — and ask it questions about the whole document. You can say "extract every requirement from Section C and map each one to the evaluation criteria in Section M" and the model has access to both sections simultaneously.

This is not an incremental improvement. It is the difference between AI being a toy for govcon and AI being an operational tool. When the model can see the entire RFP, it can identify cross-references between sections, catch inconsistencies between the SOW and the evaluation criteria, and build a compliance matrix that actually reflects the full document.

AI capabilities mapped to your workflow

Here is a concrete mapping of what current AI can do across the govcon lifecycle. No hype. Just capabilities that are working in production today at government contractors.

Capture and BD:

• Read a SAM.gov opportunity notice and produce a structured opportunity brief (agency, requirement summary, NAICS, set-aside status, key dates, evaluation approach)
• Analyse FPDS award data for a specific NAICS code or agency to identify incumbent contractors, award values, contract types, and competitive patterns
• Generate a first draft capture plan from opportunity data, competitive intelligence, and your company's capabilities
• Score a bid/no-bid decision against a structured framework using opportunity data

Proposal development:

• Shred an RFP: extract every shall/will/must requirement from the SOW/PWS and Section L, and organise them by evaluation factor
• Build a compliance matrix mapping Section L requirements to Section M evaluation criteria with proposal section references
• Generate first drafts of technical approach, management approach, staffing plan, and transition plan sections
• Draft past performance narratives from project descriptions and CPARS data
• Check a draft proposal against the compliance matrix to identify gaps

Contract performance:

• Generate monthly status reports from project data in CDRL-compliant formats
• Draft CPARS narratives from performance data
• Maintain and update risk registers with AI-assisted risk identification
• Synthesise lessons learned from project documentation

Compliance:

• Analyse FAR/DFARS clauses in a contract and flag compliance requirements
• Organise documentation for DCAA audit readiness
• Screen for organisational conflicts of interest (OCI) against new opportunities

The hard limits: what AI cannot replace

Being clear about AI's limitations is not pessimism — it is how you avoid wasting time and producing bad output. Here is what AI cannot do in government contracting, and why.

AI cannot know your solution. When you respond to an RFP, your technical approach is built on your company's specific methodology, tools, proprietary processes, and past experience. AI does not know any of this unless you tell it. A first draft generated without this context will be generic — the kind of "we will leverage our proven methodology" language that evaluators see in every losing proposal. You must feed AI your solution details, your differentiators, and your approach before it can produce anything useful.

AI cannot replace your subject matter experts. The government evaluator reading your proposal wants to see that your team has deep expertise in the specific domain. AI can draft the structure and initial language of a technical section, but the substantive technical content — the approach that demonstrates you actually know how to solve the problem — must come from your SMEs.

AI cannot attend oral presentations. If the procurement includes orals, your team needs to present and answer questions in real-time. AI can help prepare briefing materials and anticipate questions, but the presentation itself is a human activity.

AI cannot make the bid/no-bid decision. It can score an opportunity against your framework and provide data to inform the decision. But the strategic judgment — "Is this aligned with our growth strategy? Do we have the right relationships? Can we win this?" — requires human leaders who understand the business.

AI cannot guarantee compliance. It can build a compliance matrix and check a draft against it, but the final compliance review must be done by a human who understands the nuances of government procurement. A missed requirement in Section L can get you eliminated regardless of how strong your technical approach is.

CUI, ITAR, and FedRAMP: the govcon-specific constraints

This is the section that matters most for govcon and that most generic AI courses ignore entirely. The question is not "Can AI help with proposals?" — the answer is obviously yes. The question is "Can I put this data into an AI tool without violating my security obligations?"

Controlled Unclassified Information (CUI): Many government contracts involve CUI — information that is not classified but is controlled under 32 CFR Part 2002. If your RFP, SOW, or contract data contains CUI markings, you need to understand whether your AI tool's data handling meets NIST SP 800-171 requirements. Most commercial AI tools (ChatGPT, Claude, Gemini) in their standard consumer configurations do not meet these requirements. Enterprise and API versions with appropriate data handling agreements may be acceptable — but you need to verify this with your contracts and security team.

ITAR (International Traffic in Arms Regulations): If you work on defence contracts covered by ITAR, the restrictions are more severe. ITAR-controlled technical data generally cannot be processed by cloud-based AI tools unless the tool is hosted in a US-only environment with appropriate export control safeguards. This is a hard constraint for many defence contractors.

FedRAMP: FedRAMP authorisation means a cloud service has been assessed against federal security standards. Some AI providers have FedRAMP-authorised offerings (Microsoft Azure OpenAI Service has FedRAMP High authorisation, for example). Using a FedRAMP-authorised AI tool significantly reduces your compliance risk — but does not automatically mean all data types are permitted.

The practical framework: For most mid-size contractors, the safe starting point is to use AI on data that is not CUI, not ITAR-controlled, and is already publicly available or company-internal. This includes: publicly posted RFPs and solicitations from SAM.gov, FPDS data (which is public), your own company capabilities descriptions, generic proposal templates, and capture plan structures. As you mature, you can evaluate FedRAMP-authorised AI tools for handling more sensitive data.

AI and the structure of government documents

Government documents follow rigid structures that AI is exceptionally good at parsing. This is actually one of the reasons AI works better for govcon proposals than for many other business writing tasks.

A federal RFP follows a standard structure defined by FAR Part 15: Section A (Solicitation/Contract Form), Section B (Supplies or Services and Prices/Costs), Section C (Description/Specifications/SOW), through Section M (Evaluation Factors). Every proposal professional knows this structure. More importantly, AI models have been trained on thousands of these documents, so they understand the relationships between sections.

When you tell AI "Extract the evaluation factors from Section M and map them to the instructions in Section L," the model understands what Section L and Section M are, how they relate to each other, and what the output should look like. This is not true for arbitrary business documents — but government procurement documents are structured enough that AI can reliably parse them.

The same applies to other govcon document types. CDRLs follow DID (Data Item Description) formats. CPARS evaluations use standard rating scales (Exceptional, Very Good, Satisfactory, Marginal, Unsatisfactory). FAR clauses are numbered and referenced consistently. Contract line item structures follow predictable patterns. This structural consistency is an advantage when using AI — it means the model can reliably identify and extract the information you need.