Generate realistic story data for your demo environment
Generates a coherent demo dataset — realistic records in your prospect's industry language, deliberately planted records that make each demo beat land, background texture so the plants don't stand alone, and living timestamps. Replaces 'Test Company 123' with set design that lets prospects see themselves in the product.
You are a demo environment designer — the person presales teams call when their demo instance is full of 'Test Company 123' and users named Asdf. You know demo data is set design: when a prospect sees their own industry's terminology, realistic deal sizes, and plausible names on screen, they project themselves into the product. When they see lorem ipsum and $1,000,000 test deals, the demo becomes fiction. You also know the data must support the STORY — if the demo's key moment is catching a stalled deal, the data must contain exactly one perfectly stalled deal to catch.
Generate story data for my demo environment. Output format:
1. THE CAST: 8-12 realistic records appropriate to my product (contacts, companies, deals, tickets, shipments — whatever my objects are), with names, values, and attributes matched to my prospect's industry and size. Diverse, plausible names; realistic amounts for the segment; industry-correct terminology in every text field.
2. THE PLANTED MOMENTS: for each key beat in my demo storyline, the specific record engineered to make it land — the stalled deal, the overloaded rep, the anomaly the dashboard catches — with a note on which beat it serves.
3. THE BACKGROUND TEXTURE: what the unremarkable records should look like so the planted ones stand out naturally instead of being the only data.
4. THE TIMESTAMPS: relative dates ('4 days ago', 'last Tuesday') so the environment reads as alive, with a warning list of any data that will look stale by demo day.
5. THE FORMAT: delivered as a table or CSV-style block I can import or key in quickly.
Rules: no real company names as customers, no joke names, no data that could embarrass anyone if screenshotted. Every number must survive scrutiny from someone who does this job daily.
Before you write anything, interview me. Ask me these questions ONE AT A TIME, waiting for my answer each time:
1. What's your product, and what objects live in it — deals, tickets, campaigns, orders?
2. Who's the prospect — industry, size, and what their daily volume looks like?
3. What are the 2-3 key moments in your demo where the data must do the work?
4. Any terminology quirks in this industry I should reflect — what they call customers, deals, jobs?
Once you have my answers, generate the data. If I don't know their real-world volumes, ask one follow-up — wrong-scale numbers are the fastest way to lose a practitioner's trust.How to use it
- 1
Copy the prompt into Claude, ChatGPT, or any LLM.
- 2
Build the storyline first (the demo storyline prompt in this category pairs directly) so question 3 has real answers.
- 3
Load the data the day before, and check the timestamp warning list on demo morning.
- 4
Walk through the demo once against the new data — every beat should now have its exhibit.
- 5
Keep per-industry datasets saved; regenerating the cast for a new vertical takes five minutes.
Best practices
Scale is the credibility test — a 50-person company with 4,000 open deals reads as fake to any practitioner instantly.
One planted anomaly per beat; three stalled deals dilute the moment where you catch the one.
Use the prospect's vocabulary in text fields — if they call them 'engagements', no record should say 'deals'.
Screenshot-safe is a hard rule; assume everything on screen ends up in their internal Slack.
Example: what this looks like in practice
An SE at a CRM startup preps a demo for a 30-person commercial roofing company. His instance is full of SaaS-flavored test data — 'Acme Corp, $250K ARR deal' — meaningless to a roofer. He answers the interview: objects are jobs and estimates, the key moments are spotting an aging estimate and a crew scheduling clash, and roofers say 'bids', not 'deals'. The output delivers eleven records — bids like 'Westside Retail Center re-roof, $84,500', crew names, and one bid sitting quietly at 19 days without follow-up, flagged as the plant for beat one. On the demo, the owner leans in and says 'we have three of those aging bids right now' — and starts demoing the product to himself, which is precisely the reaction demo data exists to produce.
Best fit
This prompt is one gear in a bigger machine. We orchestrate 20+ tools into outbound systems our clients own — and guarantee the results.
Apply for a Pilot Spot → →Frequently asked questions
Because prospects don't evaluate features — they simulate their own life in your product. Data in their industry's language at their realistic scale lets them project themselves in; 'Test Company 123' and million-dollar placeholder deals turn the demo into fiction. Practitioners spot wrong-scale numbers in seconds, and once the data reads fake, everything on screen inherits the doubt.
More discovery calls & demos prompts
Build a discovery question bank tailored to one persona
Generates a 15-question discovery bank built around one persona's actual responsibilities and failure modes, organized by call stage, with listening notes on what strong and weak answers sound like. Beats generic question lists because the interview forces your product, competitor, and deal-death context into every question.
Write a post-discovery summary email that mirrors their words
Produces a sub-180-word summary email built on the prospect's verbatim phrases, with impact-paired bullets, an invitation to correct you, and next steps with your commitments listed first. Mirroring their language proves you listened in a way no template can, and the email becomes the artifact your champion forwards internally.
Build a demo storyline from your discovery notes
Converts raw discovery notes or a transcript into a three-act demo storyline that opens on the prospect's problem in their own words, sequences scenes by deal importance, tells you what NOT to show, and scripts the close. It's the difference between a feature tour and a demo the champion replays internally.