Verified against ChatGPT · 2026-08-11
Structure a company analysis brief around what actually differentiates it from named competitors
Organizes public information about a company's business model, financials, and competitive position into a decision-ready brief, built around explicit comparisons to named competitors rather than a generic company overview.
The prompt
Ready to copy — highlighted parts are example details you can swap.
Structure a company analysis brief. Purpose: Prepping for a partnership conversation — need to understand where this company sits relative to two potential alternative partners.. This is a research organization exercise using the information provided plus what you already know from training — not a real-time data pull, so flag anything that may be outdated and ask for current figures on the specific numbers that matter most for the stated purpose. COMPANY A mid-size logistics software vendor. WHAT I ALREADY KNOW / HAVE GATHERED Their pricing page, a recent product announcement, and last year's stated customer count of 1,200 mid-market clients. NAMED COMPETITORS Two named logistics software competitors in the same mid-market segment. KEY QUESTIONS THIS BRIEF NEEDS TO ANSWER Do they have a meaningfully different integration approach than the two competitors? Is their customer base concentrated in one vertical? Structure the brief in this order: (1) a two-sentence positioning statement — what this company actually sells and to whom, stated concretely rather than as marketing language lifted from its own materials; (2) a direct, named comparison against each competitor listed, one differentiator and one disadvantage per competitor, not a generic strengths/weaknesses list that could apply to any company in the sector; (3) the financial or operational signals available that are most relevant to the stated purpose, clearly separating anything from the provided information versus anything from general background knowledge, and flagging your knowledge cutoff limitation explicitly wherever a current figure (stock price, latest quarter's revenue, recent funding round) would materially change the analysis; (4) direct answers to the key questions listed, or an explicit statement of what current data would be needed to answer them properly. Do not present a stale or possibly outdated figure as if it were current — mark it as "as of my training data" or "as provided" wherever precision matters. Do not recommend an investment action; this is a positioning and structure exercise, not investment advice. OUTPUT FORMAT 1. Positioning statement. 2. Competitor-by-competitor comparison table: competitor | this company's edge | this company's disadvantage. 3. Relevant signals, source-tagged (provided vs. background knowledge vs. needs current data). 4. Answers to key questions, or explicit data gaps.
Customize
Optional — swap in your own details for the highlighted parts above.
Why this works
Forcing a named, per-competitor comparison rather than a generic SWOT-style list is what makes the brief specific rather than interchangeable — a strengths/weaknesses list written without a named competitor in the sentence tends to read as boilerplate that could apply to any company in the category, while "here is exactly what this company does better and worse than competitor X specifically" forces a real comparison the model can't fake without engaging the actual details given. The source-tagging requirement (provided information vs. background knowledge vs. needs current data) is the most important mechanical safeguard here: GPT-5.1's training data has a cutoff, and financial or competitive positioning can shift meaningfully in months, so a company analysis that blends a possibly stale funding figure or headcount number in with fresh information the requester just supplied — without marking which is which — creates a false sense that everything in the brief is equally current when some of it may be a year or more out of date. Explicitly refusing to give investment advice keeps the brief in its actual lane: structuring and organizing a competitive picture is something the model can do well from provided and general knowledge, but recommending a financial action requires real-time data, risk tolerance, and legal considerations well outside what a training-data snapshot and a prompt can responsibly provide. The explicit data-gap section at the end converts "the model doesn't know" from a silent failure into a specific, actionable next step — a list of exactly what to go look up.
What you get back
Positioning: sells routing and dispatch software to mid-market trucking fleets, priced per-vehicle rather than per-seat. Versus Competitor A: edge — deeper integration with common ELD hardware; disadvantage — smaller customer base limits network-effect data for route optimization. Flag: 1,200 customer figure is as provided by requester, not independently verified or current.
Verified against
ChatGPT GPT-5.1 · 2026-08-11
Changelog
- 2026-08-11 — Initial publish, verified against ChatGPT GPT-5.1.
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