Stop Staring at a Blank RFP: A 5-Step Editor-First Response Workflow

Filling out an RFP (request for proposal) is like navigating a labyrinth of expectations, requests, and demands. Security wants encryption language that matches last year's audit packet. Sales wants sharper differentiators, and legal wants nothing that wanders off-script. Meanwhile, the deadline is not a suggestion.

That blank page can easily become a bottleneck when you're starting from zero every single time. The good news is that winning language already lives in knowledge base articles, closed RFPs, and security white papers. The bad news is it takes even longer to find that information than to simply start from scratch.

Scott Pulley, solutions engineer at Domo, addressed this challenge in a recent Domo livestream. He demoed an RFP Response Agent, walking a five-question sample RFP for ABC Corp through governed sources, multi-voice drafts, and human's edits and approval.

In this blog, we're sharing Scott's five-step editor-first workflow you can apply to RFPs and security questionnaires.

Collect governed source material first

First, you assemble the corpus the AI agent is allowed to trust. Scott grounded the RFP Response Agent in three kinds of material most organizations already hold:

• knowledge base articles
• winning past RFPs that closed deals
• security whitepapers with certifications and encryption language.

That's because knowledge article explains how a feature works. A closed proposal shows language a buyer already accepted. A security white paper supplies the phrasing audit teams expect on controls and certifications.

Treat this as foundation work on governed unstructured content, not a free-for-all scrape of every shared drive. Index approved documents so retrieval can surface passages with confidence. Domo Documents can serve as that vector backbone for unstructured files, with AI search returning chunks the team can inspect.

Also, know you can keep the system of record where it already lives, because Domo natively integrates with Snowflake, Databricks, and Google.

Draft from sources, not a blank page

With these sources in place, drafting becomes retrieval-augmented writing rather than creative improvisation. Scott's demo started with an uploaded RFP. The agent read the questions, searched the indexed corpus, and produced answer drafts grounded in those documents. Nobody had to invent from a blank page.

The AI-generated text stayed grounded in retrieved organization documents through RAG on Domo Documents. The platform coordinates retrieval and drafting on that governed content, and a person still decides what ships.

RFP questions rarely surprise anyone who has answered them before. What burns time is re-finding the right paragraph, restating it cleanly, and checking that nothing drifted from policy.

Source-grounded drafting attacks that loop. The agent proposes, the corpus constrains, and the person decides. If a draft cannot point back to the index, treat that as a red flag. Bounded autonomy keeps the system inside sources leadership has already approved.

Generate multiple voices, then pick for the buyer

One draft is rarely enough. Buyers read the same security question with different appetites for depth. So, Scott generated three voice options per question: concise, comprehensive, and differentiator. After discovery calls, those voices can be tuned so the tone matches what the buyer wants to hear.

Concise answers, for instance, help when an evaluator is skimming a long matrix and wants a clean yes with little extra detail. Comprehensive answers help when a security review needs every control to have room. Differentiator answers help when the commercial team needs more than a checkbox while staying faithful to source material.

Producing all three in one pass turns voice from a late-night rewrite into a selection problem. Proposal leads can match the voice to discovery notes instead of arguing about tone after a single draft hardens. The same passages can support short or long answers. The difference is emphasis and length, not new facts. When five people rewrite five sections in five styles, the document frays, but structured voice options from one corpus make style a controlled choice.

Edit and approve with source visibility

Here's the centerpiece of the workflow: The agent does the first heavy lift, but a person still owns the personal touch, the judgment call, and the quality bar.

For each answer, Scott's flow showed which documents and passages informed the draft. So, when the human reviews, they can verify details such as:

• Does this paragraph match the white paper?
• Does this claim still hold after the last product change?
• Does legal need a narrower verb?

Source-linked review lets people correct tone, add a customer-specific detail, or reject a stretch without rebuilding from scratch. That visibility makes bounded autonomy practical for proposal and security teams.

Export a branded customer-ready packet

After approval, packaging should not reopen the content debate. Scott closed the loop by exporting a branded PDF with logo treatment and Domo blue styling. The packet looked customer-ready instead of like a chat dump.

Distribution, then, is part of the outcome. A governed answer that never leaves the workbench doesn't help the deal, but a clean export carries approved language, consistent voice, and brand presentation so sales and security hand over the same artifact.

Tool consolidation shows up here as a practical benefit. Many organizations already pay for a separate RFP point tool beside their data and AI stack. Response work on the same agentic platform keeps AI features inside existing consumption rather than another island.

Time savings come from fewer handoffs, and reliable sourcing comes from one approved corpus. Run the five steps as a loop so each closed win returns to the library and each white paper update refreshes the index. The editor gets stronger because the sources get better.

Watch the full session for the build and the broader pattern

The five steps above give proposal and security teams a workflow they can run without watching a minute of video. The livestream still earns a look for the parts this resource only sketches.

Scott built the full RFP Response Agent in a couple of hours because the platform carried the backend lift on a Domo Documents backbone. AI search over chunks and text generation prompted for voice sat on top of retrieval-augmented grounding. Mark and Scott also framed the pattern beyond RFPs. That includes invoices and other unstructured documents, plus consolidating response work instead of keeping a standalone RFP point solution.

Watch the full session to see the demo end to end and catch those build details live.