CRAFT Content report
Case Study Generator: your result
This answer uses a made-up example company. Change the details above and press Run to see your own result.
Case Study Generator
Free online B2B case study generator
For content and product marketing leads. The decision: how do we turn one customer result into a B2B case study today?
Paste in the customer, your product, the challenge, the solution and the result, and get a B2B case study with key takeaways, plus short versions for a testimonial page, social media and email. It is free, it runs online and there is no sign up.
What you get
This sample is a made up example, built from the inputs behind Fill in an example. It shows part of one section of the answer.
The Story (draft)
The situation. A food delivery company is in the Food delivery segment. (General note for AI native, not from your inputs: teams are usually judged on automated resolution rate and accuracy on an evaluation set.)
The challenge. Before our product, the team was dealing with this, in your words: a ticket backlog growing faster than the support team could hire.
The approach. The team put our product in place: AI agents that resolve refund and order-status tickets inside the help desk, with human approval for refunds.
The result. 45% of tickets resolved without a human in 60 days. First response time down from 6 hours to 2 minutes.
See the full example answer or run it with your own details.
Result
When a tool is not enough: if the decision needs more than one answer, talk it through in a call. Book 45 minutes with Shashwat.
What next? Work with Shashwat Try another tool See all 85 tools and skills
Example answer
Generated on 2 November 2026 from this branch, with the inputs behind Fill in an example. Your own inputs give a different result.
CRAFT Content report
Example: Case Study Generator
Show the full result
Case Study: a food delivery company
45% of tickets resolved without a human in 60 days
Sector: read from your inputs as AI native. Business model: not stated. Tell us how you charge and the plan will adjust.
Billing dependent steps are conditional: trials, seats and recurring renewals require a subscription; a statement of work requires a services contract; site or link commitments require connectivity billing. Confirm the applicable choice before using those steps.
Built from: challenge: given; solution: given; results: 2 items (2 outcomes); customer quote: not given; industry: given.
The Story (draft)
The situation. A food delivery company is in the Food delivery segment. (General note for AI native, not from your inputs: teams are usually judged on automated resolution rate and accuracy on an evaluation set.)
The challenge. Before our product, the team was dealing with this, in your words: a ticket backlog growing faster than the support team could hire.
The approach. The team put our product in place: AI agents that resolve refund and order-status tickets inside the help desk, with human approval for refunds.
The result. 45% of tickets resolved without a human in 60 days. First response time down from 6 hours to 2 minutes.
To add before this is published: (1) one line a food delivery company would say aloud about what changed, approved in writing.
The sections below hold the detail behind each part of the story and the questions that finish it.
About a food delivery company
| Customer | a food delivery company |
| Industry | Food delivery |
| Product | our product |
| Lead result | 45% of tickets resolved without a human in 60 days. |
The Challenge
What the team was dealing with: A ticket backlog growing faster than the support team could hire
To finish this section (ask the customer, and use their words):
- What the problem cost them, with the starting value of one of: automated resolution rate, accuracy on an evaluation set and escalation rate.
- Interview question in the sector's language: "Which cases or decisions would you trust an AI to handle first, and which never?"
The Solution
What was put in place: AI agents that resolve refund and order-status tickets inside the help desk, with human approval for refunds
To finish this section:
- Why a food delivery company chose our product, and which alternative it weighed (a common reaction in AI native: "AI gets answers wrong").
- How the rollout went, and how long it took to see first results. Use only a duration the customer confirms.
- Words to use as the customer says them: resolution rate, evaluation set, human in the loop, guardrails, accuracy.
The Results
Outcomes
- 45% of tickets resolved without a human in 60 days.
- First response time down from 6 hours to 2 minutes.
To finish this section: for each figure, give the value before, the value after and the period it was measured over. A strong proof point here: Results on an evaluation set built from the buyer's own history, then live results with human review switched on.
In Their Words
No customer quote was given. Ask for one line the customer would say aloud about what changed in automated resolution rate, and get written approval before you print it.
Where This Goes Next
Close with one action for the reader. Deals in AI native usually start like this: a proof of concept on the buyer's own data, then a production pilot with guardrails and human review. Write the reader's next step to match, for example a pilot request.
Sector Notes: AI native
- A strong proof point here: Results on an evaluation set built from the buyer's own history, then live results with human review switched on.
- What readers in this sector measure: automated resolution rate, accuracy on an evaluation set, escalation rate, handling time, cost per resolution, customer satisfaction on automated cases.
- Terms this sector's buyers use: resolution rate, evaluation set, human in the loop, guardrails, accuracy, hallucination, data privacy, inference cost, automation rate.
- Your results: they carry a figure.
Distribution Formats
One-line version (testimonial pages)
45% of tickets resolved without a human in 60 days (our product).
Social media version
45% of tickets resolved without a human in 60 days.
Product: our product.
Read the full story: add the link when the case study is published.
Email snippet
Quick success story: 45% of tickets resolved without a human in 60 days. Add the link to the full story when it is published.
Check Before You Publish
- The customer has approved the story, every figure and the quote in writing.
- Every figure comes from the customer or a page you can cite; page claims stay labelled as the vendor's own claims.
- Nothing in the recognition or company-wide claims is shown as this customer's result.
When a tool is not enough: if the decision needs more than one answer, talk it through in a call. Book 45 minutes with Shashwat.
How to read the answer
This draft structures what you entered into The Story (draft), The Challenge, The Solution, The Results, In Their Words, Where This Goes Next, Sector Notes, and Distribution Formats without inventing customer quotes or figures. Wherever a baseline, measurement period, or direct quote was left out, the output inserts a question to ask the customer rather than making up a number. Treat the entire output as an internal working draft until you fill the gaps and get written customer sign-off under Check Before You Publish.
What to do next
- Founder: Send the questions marked in The Challenge, The Results, and In Their Words to your customer contact and ask for written permission to publish their name and numbers.
- CEO: Check Check Before You Publish to confirm that every figure has a baseline, a measurement period, and written customer approval before using the story in investor or board decks.
- CMO: Once the customer approves the text, hand the Distribution Formats (One-Line Testimonial, Social Media Version, and Email Snippet) to your content team and update the proof strip on your website.
- CRO: Give the approved story and the Sector Notes proof shape to account executives to use in deals facing the same buyer role and industry.
- Demand generation head: Put the approved Email Snippet and Social Media Version into your nurture sequences and retargeting campaigns for that sector.
Why come back
Run the tool in Discovery mode before your next customer interview to get the exact questions to ask, then return in Full mode once you have the customer's transcript, before-and-after numbers, and approved quote. The tool applies the same fixed rules every time. It does not call an AI model, and this site does not keep what you enter.
Who is the Case Study Generator tool not ideal for?
- Anyone who cannot give the inputs listed above, because the answer is built only from them.
- Teams that want finished copy in their brand voice: each tool returns a structured draft to edit, with placeholders wherever your inputs did not supply a fact.
- Anyone who needs research or quotes gathered for them: the tools do not look anything up. They work from the story and facts you give, and anything they add themselves is marked as an example, a suggestion or a placeholder.
How do I use the Case Study Generator tool with an AI assistant?
Add CRAFT Content once (see Use it in Claude, ChatGPT or Gemini), then ask in plain words. Here is a prompt built from the tested example:
My role is not stated. Ask me for my role first. Use CRAFT Content's Case Study Generator tool. customer name: a food delivery company; your product: our product; customer industry: Food delivery; mode: Full; challenge: A ticket backlog growing faster than the support team could hire; solution: AI agents that resolve refund and order-status tickets inside the help desk, with human approval for refunds; results: 45% of tickets resolved without a human in 60 days; first response time down from 6 hours to 2 minutes. If this tool is unavailable, ask me to open its web page; do not invent its output. In Gemini CLI or Antigravity, use the Helix GTM Suite skills package for related guidance, without claiming to run this connector.
How does the Case Study Generator tool work?
You do not need to sign in. A web browser is enough, and the answer comes back in a second or two.
- Fill in the form above, or press Fill in an example.
- Press Run Case Study Generator.
- Read the result on this page, then copy it and edit it before you use it.
It applies fixed rules in the CRAFT Content code to the inputs above and returns text. It does not call an AI model, does not look anything up, and does not store what you type. The web form and the AI assistant connector run the same tool code, so the same input gives the same answer in both.
For developers
MCP tool name: case_study_generator. REST: POST https://craft-content.gtmhelix.com/api/tools/case_study_generator with this JSON body (see How it works):
{
"input": {
"customer_name": "a food delivery company",
"your_product": "our product",
"customer_industry": "Food delivery",
"mode": "full",
"challenge": "A ticket backlog growing faster than the support team could hire",
"solution": "AI agents that resolve refund and order-status tickets inside the help desk, with human approval for refunds",
"results": "45% of tickets resolved without a human in 60 days; first response time down from 6 hours to 2 minutes"
}
}
Choose your app
Run a Helix tool in your browser, then take the result to ChatGPT, Claude or Gemini. Connectors work with supported plans in Claude, ChatGPT, Cursor, VS Code and Windsurf. The Helix GTM Suite skills package supports Claude Code, Gemini CLI, Antigravity, Cursor and VS Code, and is available through skills.sh. Each plugin has its own installation steps, below or on its own page.
Connector setup guides · Skills package setup guide · Find the skills on skills.sh