How to Use AI for RFPs: A Guide for Solutions Engineers

AI for RFPs changes this dynamic. Not by automating the judgment calls — those still belong to you — but by handling the surrounding work: classifying questions, drafting initial responses, surfacing relevant past answers, and checking the final document for consistency. This guide walks through six steps to integrate AI into your RFP workflow, starting with the first document you receive and ending with a knowledge base that makes every future RFP faster than the last.

What You’ll Need

An AI assistant that supports persistent context across sessions (so your company positioning, product capabilities, and past responses are available without re-loading them every time)
Your existing RFP assets: past responses, product capability documentation, security FAQ, standard pricing language
One recent RFP to use as your starting point — ideally one with at least 40 questions across multiple categories

How to Use AI for RFPs: 6 Steps That Actually Work

Step 1: Classify the RFP Before You Write a Single Word

Most SE teams start an RFP by opening the spreadsheet and working top to bottom. The problem is that this treats all questions the same, when they’re not. Security questions need a compliance review. Technical architecture questions need your product team’s sign-off. Company background questions are largely templated. Mixing these together means you lose track of what needs escalation and what can be drafted immediately.

Before any drafting starts, use AI to read the full RFP and produce a classified breakdown. You want to know: which questions you can answer directly, which require subject matter expert input, which are already answered in your standard library, and which are genuinely new — either because the customer is unusually thorough or because they’re testing a specific concern.

Try this with Noumi: “Here is an RFP with 94 questions. Categorize each into one of five buckets: Security & Compliance, Technical Architecture, Integrations & APIs, Commercial & Pricing, and Company Background. For each question, flag whether it likely requires external SME input (legal, security, finance) and estimate the complexity as Low, Medium, or High.”

Example output structure:

Security & Compliance: 31 questions (4 flagged for legal review)
Technical Architecture: 22 questions (7 flagged for product team)
Integrations & APIs: 14 questions
Commercial & Pricing: 11 questions (2 flagged for finance)
Company Background: 16 questions

Tip: Run this classification before your kickoff call with the account team. It turns the kickoff from “let’s read through this together” into “here’s what we can draft this week and here’s what we need from legal by Thursday.”

Step 2: Build a Shared Context Document Your AI Knows

The reason most SEs get inconsistent output from AI is the same reason output from a new team member in their first week is inconsistent: no context. Every session starts from zero. You ask for a response to a data residency question, and the AI has no idea whether you’re SOC 2 Type II certified, which regions your infrastructure covers, or how you’ve answered this question in the past.

Before drafting any RFP responses, build a shared context document that covers the information you need every time: company size and stage, product architecture overview, key security certifications, standard integration partners, pricing model (high level), and your top three differentiators with the language your team actually uses. Store this where your AI can reference it at the start of every RFP session.

Solutions engineers who work in platforms built around persistent context across pre-sales projects find this step pays for itself by the second RFP — the context is already there, refined from the last session, rather than being rebuilt from scratch.

Try this with Noumi: “Here is our company context document: [paste]. We’re a Series B B2B SaaS company. Our key certifications are SOC 2 Type II and ISO 27001. Our infrastructure runs on AWS in US-East, EU-West, and APAC-Sydney regions. Use this context for all RFP responses we work on together.”

Step 3: Draft Technical Sections First, Not Last

Technical sections are where RFP responses lose the most time. The default pattern is to save them for last — after the easy company background and pricing questions — which means the hardest, most accuracy-sensitive content gets written under the most time pressure.

Flip this. Use AI to produce a first draft of your technical sections on day one, before other work starts. The draft will need review, but having something accurate to edit is faster than writing from scratch, and it gives your technical SMEs something specific to react to rather than a blank page.

The key to getting useful technical drafts is specificity. Vague inputs produce vague outputs. Give the AI the exact question, your product’s actual capabilities (from your context document), and any constraints on how the answer should be framed — word count, technical depth, and the audience (a procurement reviewer reads differently than a CISO).

Try this with Noumi: “Draft a response to this RFP question: ‘Describe your approach to data encryption for data at rest and in transit, including the encryption standards used and key management practices.’ Base the response on our documented capabilities in the context document. Target 200 words. The reader is a procurement manager, not a security engineer — clear and specific, not overly technical.”

Tip: Always review technical drafts against your actual security documentation before including them in a submission. AI drafts are starting points, not final answers — especially for compliance-sensitive content.

Step 4: Adapt Past Responses Instead of Starting Over

One of the most valuable patterns in RFP work is also one of the most underused: your previous submissions contain high-quality, reviewed, approved answers to questions that appear — in different forms — in almost every RFP you’ll ever receive. The question about data residency in the enterprise software RFP from Q1 is 80% the same as the question in the healthcare compliance RFP from Q3. The problem is that most teams either don’t save these answers, or save them in a format that makes retrieval slow and adaptation slower.

Use AI to actively mine your past submissions. Feed in a previous RFP response document and ask it to find answers that are relevant to new questions — even when the phrasing is different. Then use AI to adapt those answers to the new context rather than writing from scratch. This is where the compounding value starts to show: each well-answered RFP becomes source material for the next one.

Try this with Noumi: “Here is our completed RFP response from the Meridian Financial deal. The new RFP from Apex Health is asking about multi-tenant data isolation — Question 47. Find the most relevant answers from our Meridian response that address this topic, and adapt them for a healthcare compliance context. Flag anything in the Meridian answer that doesn’t apply to the healthcare regulatory environment.”

Step 5: Run a Consistency Check Before You Submit

Long RFPs have a consistency problem that’s hard to catch manually. By the time you’ve drafted 80+ answers across multiple contributors over several days, the document often contradicts itself: one section says the product supports a certain deployment model, another section implies it doesn’t. A pricing question answer uses language that conflicts with the commercial terms section. Two different team members described the same integration in ways that don’t match.

A consistency check pass before submission catches these issues in minutes rather than the hours a manual review would take. Use AI to read through the full response document and look for contradictions, inconsistencies with your documented capabilities, and places where the same concept is described in conflicting ways.

Try this with Noumi: “Review these 68 completed RFP answers for internal consistency. Flag: (1) places where the same feature or capability is described differently across sections, (2) any answer that contradicts a claim made in another answer, and (3) any answer that uses language inconsistent with the product capabilities in our context document.”

Tip: Do this check the day before the deadline, not the hour before. Catching a contradiction that requires a product team clarification at 11pm is avoidable stress.

Step 6: Store Every Good Answer in a Living RFP Library

The goal of AI-assisted RFP work isn’t just to respond faster this time — it’s to make every future RFP faster and better. That only happens if you capture what you learned. Every well-crafted answer, every new question type you hadn’t seen before, and every approved technical explanation should be stored where your AI can find and reuse it.

Most SE teams lose this value because RFP responses get filed in shared drives that are never systematically searched again. A better approach: after each RFP closes, run a session to extract the most reusable answers, format them as templates with clear placeholders for deal-specific details, and add them to your knowledge base.

This is the compounding return on AI in RFP work. The first RFP takes the same time it always did. The fifth one takes meaningfully less. By the tenth, you’re primarily reviewing and editing rather than drafting from scratch.

Try this with Noumi: “Based on the approved final submission from the Apex Health RFP, extract the 8 answers most likely to recur in future deals. Format each as a template with [PLACEHOLDER] fields where we’d customize per customer — customer name, region, specific regulatory framework. Add a note for each about what context shaped the specific framing we used.”

Pro Tips for SEs Managing RFP Workflows with AI

Involve SMEs before drafting, not after. The most common way AI-assisted RFPs go wrong is drafting a technical answer, sharing it with a SME for review, and getting it substantially rewritten. Run your Step 1 classification past your SMEs before any drafting starts — let them flag the questions that need their direct input, and draft everything else in parallel.

Keep a separate file for deal-sensitive context. Your general company context document is useful across all RFPs. But each specific deal may have context that shouldn’t live in a shared library — customer pain points from the discovery call, sensitivities around pricing or competition, internal notes on the deal dynamics. Keep this in a separate session context that stays deal-specific.

Don’t treat the AI draft as the submitted version. AI is genuinely useful for RFP drafting, but the judgment layer — what to include, what to leave out, where to be conservative — belongs to the SE. Review every answer, especially in the security and compliance sections.

Track which answers get scrutinized. After a deal closes (win or loss), note which RFP sections the customer came back to during evaluation. Over time, this tells you which questions carry the most weight in your buyer’s decision process and which answers are worth the most investment.

Frequently Asked Questions

How do I handle RFP questions about roadmap features that aren’t live yet? +

The general rule: be specific about what’s available today, and if you mention roadmap items, use qualifying language (“planned for Q3,” “currently in beta”) — and confirm with your product team before including it. AI can help you draft the present-state answer clearly; the roadmap framing is yours to own.

Can the same workflow apply to security questionnaires, not just formal RFPs? +

Yes, and security questionnaires are often a better starting point than full RFPs. They’re typically shorter, more formulaic, and have higher answer-reuse rates across customers. Building your AI workflow on a security questionnaire first gives you a faster feedback loop before applying it to a full 200-question RFP.

How do I manage AI-assisted RFP work when multiple SEs or SMEs are contributing? +

The context document becomes critical. Establish one central context document that everyone references, and do a single consistency check pass at the end rather than trying to align answers in real time. AI is the integrating layer — it can spot inconsistencies across sections written by different people faster than any human reviewer.

Is there a risk that AI-generated RFP answers sound generic? +

Yes, if the context document is thin. Generic input produces generic output. The more specific your context — your actual certifications, your real architecture, your specific differentiators with the language your team uses — the more specific and credible the output. Review the first few answers carefully and correct any places where the phrasing sounds templated rather than specific to your company.

How long does it take to see meaningful time savings? +

Most SE teams see a meaningful difference in their second or third RFP, not the first. The first cycle is where you build and refine the context document and answer library. By the third cycle, teams typically report that drafting is significantly faster and the review cycle is shorter because answers are more consistent from the start.

Where should SE teams store their RFP answer libraries? +

Anywhere your AI can access them at the start of a session. Teams working on a platform where context carries across every pre-sales engagement get the most value here, because the answer library is automatically available rather than requiring a manual paste at the start of each session. If you’re working with a general-purpose tool, a well-organized single document that you load at session start is the next best option.

Getting Started

The clearest entry point is the next RFP that arrives. Run Step 1 — the classification pass — before your team’s kickoff call. You’ll have a categorized question list ready in minutes rather than spending the first thirty minutes of the call building it together.

The SEs who build lasting AI workflows around RFPs aren’t the ones with the most sophisticated setup. They’re the ones who treat each RFP as an opportunity to improve the context document, capture reusable answers, and make the next cycle a little more systematic than the last.

If you’re evaluating tools built for the kind of context-heavy, multi-project work that defines pre-sales, Try Noumi →

How to Use AI for RFPs: 6 Steps That Cut Response Time for SEs