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Proposal Research In Minutes: The 2025 AI Playbook For Bids, Tenders, and RFPs

How to research for proposals.

Published on September 8, 2025

How I Turned Days of Proposal Research Into Minutes in 2025

You don't have three days for customer research anymore.

You still need a proposal that feels written for one buyer. This guide shows how to get a cited research brief in about 15 minutes, turn it into a personalized first draft, and keep reviewers happy by showing sources and uncertainty. Copy the prompt, run the safety check, and ship a better draft today.

TL;DR

  • Get a cited research brief in ~15 minutes.

  • Turn it into a personalized first draft that references real sources.

  • Use a 2-minute safety check so nothing risky slips through.

  • Measure time saved and win-rate lift so you can prove the value.

Why personalization still wins

Personalized proposals win because buyers expect proof that you understand their world. They're receiving more proposals than every before because of AI- but most of those proposals are generic and won't win.

In 2025, you can do this fast and with citations. Large-scale studies from McKinsey and HBR link personalization to meaningful revenue and win-rate gains.*

The 15-minute research setup

It is 8:45 pm. Kickoff is at 09:00 tomorrow morning, and I haven't done my customer research yet! I open a research agent (an AI assistant that plans web searches and writes a brief with links) and give it one job: produce a cited summary that a proposal team can actually use.

Twelve minutes later, I have a clean dossier, quotes from stakeholders, a short competitor view, and three buying triggers.

I scan the “uncertain” items, remove two weak claims, export the brief and a small structured file for tools, then load both into my proposal platform.

At 09:00, the response team isn't guessing about our message. We are deciding.

Pick one research agent and standardize the format

Choose from:

  • OpenAI Deep Research: strong planning, controllable structure, returns a cited brief.

  • Perplexity Deep Research: fast breadth and synthesis with citations, great first pass.

Side note: I tend to use Perplexity for this, unless I am adding in another agent to pass content somewhere else.

Copy this prompt

Research a prospective buyer: <Company or Agency>. Timeframe: last 12 months.

Deliver TWO outputs:

1) Cited Brief (Markdown): - Company snapshot - Strategic priorities - Active initiatives - Buying triggers and risks - Stakeholder map (titles, quotes, sources) - Competitors and alternatives - Procurement model/calendar (if public sector) - Proposal angles and 5 win themes tied to proof Rules: Cite every claim with a working URL. If unsure, label "uncertain" and explain why.

2) Structured file for tools (JSON):

{ "claims":[{"id":"c1","text":"","evidence_url":"","confidence":0.0}], "stakeholders":[{"id":"s1","name":"","role":"","quote":"","source_url":""}], "win_themes":[{"id":"w1","theme":"","supporting_claim_ids":["c1"]}] }

Definitions just in case:

  • Research agent: an AI assistant that plans multi-step searches and returns a cited brief.

  • Structured file (JSON): a tiny machine-readable file that lets software map claims to proof automatically.

  • Retrieval: the AI finds the exact paragraph from your approved content and cites it, no manual tags.

What the AI does behind the scenes

If you use this prompt in a Deep Research agent, this is what it's doing.

Stage 1: Plan Turns your prompt into tasks, queries, target domains, and a clear output schema.

Stage 2: Gather Finds primary sources first: official site, newsroom, filings, docs, hiring pages, credible press, and public sector portals.

Stage 3: Prove or flag Writes claims with an evidence link and a confidence score. Conflicts are labeled “uncertain” with a short reason.

Stage 4: Assemble Produces a Cited Brief for us humans, plus the structured file for proposal tech. The brief should contain claims, stakeholders, and win themes.

Stage 5: Hand-off to your proposal platform Your tool ingests both, runs retrieval across your approved content, drafts answers that point to internal proof and external evidence, and builds a simple compliance table.

Stage 6: Human judgment You tune tone and differentiation, and you verify anything that impacts price, scope, compliance, security, or legal language.

The 2-minute safety check (do not skip!)

Sure, AI is cool, but it will also lie to you. So don't let it take the wheel 100%.

Here's the best way to do your own check:

  1. Filter for low-confidence claims (below 0.7) and either verify or remove (the prompt above will do this).

  2. Remove any claim that would change price, scope, compliance, or risk unless verified.

  3. Keep an Uncertainty List in the kickoff pack so reviewers know exactly where to focus.

  4. Prefer two independent sources for material assertions like “platform consolidation” or “budget freeze.”

  5. Check dates so you do not treat a 2019 priority as current truth.

Why this matters: AI systems still reward confident guesses over “I do not know.” A short trust layer protects your win rate and your reputation.

Turn research into a first draft without tagging

Older tools relied on heavy tagging and content upkeep. Modern platforms use retrieval, which finds the right paragraph at generation time and cites it. The draft should include:

  • Cited answers that point to both internal content and external evidence.

  • A simple compliance table: requirement, pointer to the answer, proof, source.

  • A brief Source Notes block at the end of each section.

If your current platform cannot ingest a structured file or retrieve with citations, keep the Cited Brief and paste the relevant claims manually. You will still save hours.

The 45-minute workflow

  • Minute 0–5: Confirm ICP, must-wins, deal risks.

  • Minute 5–20: Run Deep Research. Export the Cited Brief and the structured file.

  • Minute 20–30: Run the 2-minute safety check, then fix or remove weak claims.

  • Minute 30–40: Generate the first draft with citations and a compliance table.

  • Minute 40–45: Human edits for tone, differentiation, and final evidence links.

Edge cases to plan for

  • Public sector: Add procurement calendars, past awards, and policy changes. Favor official portals over commentary.

  • Multi-region buyers: Split insights by region to avoid false generalizations. Keep currency and fiscal year consistent.

  • Security-sensitive accounts: Redact sensitive data before sending to third-party tools. Keep an audit trail of sources.

What to measure next time you run with AI-led customer research

  • Research time per opportunity and % reuse of verified claims

  • Cited coverage in the final draft

  • Reviewer changes due to weak evidence

  • Win rate lift on personalized responses vs baseline

Call to action

Run the prompt, generate your Cited Brief, then match with 2–4 proposal platforms that support retrieval and citations. [Shortlist tools] [Browse vendors]

Find your favorites “AI-native proposal tools” on your relevant vendor pages on Stargazy.

You can shortlist the tools that look right for you, so you can catch a demo with them OR find them later when the time is right.

FAQs

How do I stop AI from making things up in my proposal? Require citations in your prompts, include an Uncertainty List, and keep human review on any claim that would change your price, scope, or compliance. (OpenAI)

What’s the minimum viable stack to start? One research agent, one proposal tool that supports citations and retrieval, and a simple Trust Layer checklist. (OpenAI, Perplexity AI)

Do I need to maintain a tagged content library? Not if your platform retrieves approved content automatically and cites sources. Many modern tools have moved past tagging.

Credits and further reading