CRAFT Content report
Webinar Script: your result
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Webinar Script
Script your webinar with timings
For demand gen and product marketing teams. The decision: how do we run a webinar that leads to meetings?
Give it the topic, the audience and the webinar type, and get a run of show, a full script with timings, Q&A preparation and a sequence of follow up emails.
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.
Run of Show
| Time | Duration | Section | Purpose |
|---|---|---|---|
| 0:00 | 2 min | Welcome & Housekeeping | Set expectations |
| 0:02 | 2 min | Speaker Introduction | Build credibility |
| 0:04 | 2 min | Agenda & Learning Objectives | Preview value |
| 0:06 | 4 min | Context Setting | Why this matters now |
Full Script
Welcome & Housekeeping (2 min)
ON SCREEN: Title slide with the webinar name
SPEAKER:
"Welcome to today's session on 'AI agents that resolve support tickets safely'. This session runs 45 minutes and is for support leaders at consumer apps and SaaS companies.
Put your questions in the chat at any time; we have set time aside for them near the end. We will run polls along the way, so keep the poll window open."
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: Webinar Script
Show the full result
Webinar Script: AI agents that resolve support tickets safely
Exercise preparation
Example needed before delivery: supply one anonymised case, its starting records and the decision participants should make. No exercise result is invented. State it as an Example line in the existing takeaway notes.
Key Takeaways for Audience
- You can say where you stand on automated resolution rate, accuracy on an evaluation set and escalation rate.
- The objection "AI gets answers wrong" can be tested, by learning to agree an evaluation on your own history before go-live, with a person approving any action that moves money or changes a record.
- Good evidence looks like this: results on an evaluation set built from your own history, then live results with human review switched on.
Run of Show
| Time | Duration | Section | Purpose |
|---|---|---|---|
| 0:00 | 2 min | Welcome & Housekeeping | Set expectations |
| 0:02 | 2 min | Speaker Introduction | Build credibility |
| 0:04 | 2 min | Agenda & Learning Objectives | Preview value |
| 0:06 | 4 min | Context Setting | Why this matters now |
| 0:10 | 8 min | Main Content Block 1 | Core teaching |
| 0:18 | 8 min | Main Content Block 2 | Core teaching |
| 0:26 | 8 min | Main Content Block 3 | Core teaching |
| 0:34 | 3 min | Summary & Key Takeaways | Reinforce learning |
| 0:37 | 6 min | Q&A | Address questions |
| 0:43 | 2 min | Close & CTA | Next steps |
Full Script
Welcome & Housekeeping (2 min)
ON SCREEN: Title slide with the webinar name
SPEAKER:
"Welcome to today's session on 'AI agents that resolve support tickets safely'. This session runs 45 minutes and is for support leaders at consumer apps and SaaS companies.
Put your questions in the chat at any time; we have set time aside for them near the end. We will run polls along the way, so keep the poll window open."
Speaker Introduction (2 min)
ON SCREEN: Speaker slide
SPEAKER:
"I am your host for this session. Over the next 45 minutes we will look at the problem, the evidence on record and what to do first. (Host: add your name and one line on the work you have done in this area.)"
Agenda & Learning Objectives (2 min)
ON SCREEN: Agenda slide
SPEAKER:
"Here is how we will use the time. Part one covers the point that you can say where you stand on automated resolution rate. Part two covers the point that the objection 'AI gets answers wrong' can be tested. Part three covers how the approach works.
By the end of this session, you should be able to say where you stand on automated resolution rate and what to change first."
Context Setting (4 min)
ON SCREEN: Context slide
SPEAKER:
"Our topic today is 'AI agents that resolve support tickets safely'.
For support leaders at consumer apps and SaaS companies, the usual yardsticks are automated resolution rate, accuracy on an evaluation set and escalation rate.
Among consumer apps and SaaS companies, the people who decide this are usually Head of Customer Experience, Chief Technology Officer and Head of Data and AI.
LAUNCH POLL: 'Which of these is closest to your situation today?'
Poll options:
- We measure automated resolution rate today and know where we stand
- We know automated resolution rate matters but do not measure it yet
- AI gets answers wrong: that is what holds us back
- This is not a priority this year"
Main Content Block 1 (8 min)
ON SCREEN: Worked example exercise
SPEAKER:
"This exercise needs an anonymised example and its starting records before delivery. Ask the host to supply them; no customer result is assumed.
The reserved 8 minutes cover the example, one participant decision record and a debrief on evidence and unknowns."
Main Content Block 2 (8 min)
ON SCREEN: Key concept slide
SPEAKER:
"That brings us to the next part. Point two: The objection 'AI gets answers wrong' can be tested, by learning to agree an evaluation on your own history before go-live, with a person approving any action that moves money or changes a record.
The measure this part speaks to is accuracy on an evaluation set. Question for the chat: 'What must a person approve before the AI acts?'
Take one case from your own work and note where it stands on accuracy on an evaluation set today.
One concern that comes up often: 'AI gets answers wrong'. A fair way to settle it is to agree an evaluation on your own history before go-live, with a person approving any action that moves money or changes a record.
Next is point three, how the approach works. Any questions before we move on? Put them in the chat."
Main Content Block 3 (8 min)
ON SCREEN: Key concept slide
SPEAKER:
"Last, and the most practical part. Point three, good evidence looks like this: results on an evaluation set built from your own history, then live results with human review switched on.
In your role, the number to watch across this part is automated resolution rate. Question for the chat: 'How would you judge that an answer is right: who reviews it today and against what?'
Before you change anything, write down where you stand today on automated resolution rate, so there is a number to beat.
Another concern we hear: 'Data privacy'. A fair way to settle it is to ask the vendor to state exactly where data is processed and stored, what is used for training (if anything) and how it can be deleted.
That covers the takeaways. Next, the summary. Any questions before we move on? Put them in the chat."
Summary & Key Takeaways (3 min)
ON SCREEN: Takeaways slide
SPEAKER:
"To sum up, you have covered the point that you can say where you stand on automated resolution rate, the point that the objection 'AI gets answers wrong' can be tested and how the approach works."
Q&A (6 min)
ON SCREEN: Q&A slide
SPEAKER:
"Now your questions. I will read each one aloud from the chat and answer it.
If the chat is quiet, I will start with a concern we hear a lot: 'We can build this on a model API ourselves'. A fair way to settle it is to compare the full cost of building and maintaining evaluation, guardrails and integrations, not the model price.
Questions we do not reach will be answered in the follow-up email."
Close & CTA (2 min)
ON SCREEN: Next step slide
SPEAKER:
"If you do one thing this week, take one live case from your own work and test it against what we covered today.
If you would like to take this further, reply to the follow-up email and ask for a conversation about the first step.
Thank you for joining."
END WEBINAR
Q&A Preparation (for the host, not read aloud)
Anticipated Questions
- "How do we get started with this?"
- Answer pattern: 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.
- "AI gets answers wrong"
- Answer pattern: Agree an evaluation on the buyer's own history before go-live, with a person approving any action that moves money or changes a record.
- "Data privacy"
- Answer pattern: State exactly where data is processed and stored, what is used for training (if anything) and how it can be deleted.
- "We can build this on a model API ourselves"
- Answer pattern: Compare the full cost of building and maintaining evaluation, guardrails and integrations, not the model price.
- "Can you share more examples of this: you can say where you stand on automated resolution rate, accuracy on an evaluation set and escalation rate?"
- Answer pattern: Use an example that follows this shape: results on an evaluation set built from the buyer's own history, then live results with human review switched on.
- "How does this compare to what we do today?"
- Answer pattern: Ask what their current setup does not do (resolution rate, evaluation set are good places to look), then show the difference on that point only.
Follow-Up Sequence
Email 1: Same day
Subject: Thank you for joining: AI agents that resolve support tickets safely
Thank you for joining.
The takeaways, in short:
- The point that you can say where you stand on automated resolution rate
- The point that the objection "AI gets answers wrong" can be tested
- How the approach works
Questions? Reply to this email.
Email 2: Day 3
Subject: A concern from the session: Cost grows with volume
The concern we hear most is "Cost grows with volume". A fair way to settle it is to ask to see the cost per resolved case against the cost of handling it today, using your own volumes.
Email 3: Day 7
Subject: Your next step after the session
The natural next step is a conversation about the first step. Reply to this email if you would like it.
Sector Notes: AI native
- Readers in AI native are usually Head of Customer Experience, Chief Technology Officer, Head of Data and AI. They watch automated resolution rate, accuracy on an evaluation set, escalation rate, handling time.
- Who sits in the buying group: The owner of the workflow being automated signs; the data or AI lead evaluates the model; security and legal review data use and privacy; the operations team that hands work to the AI uses it.
- Terms this audience uses: resolution rate, evaluation set, human in the loop, guardrails, accuracy, hallucination.
- Objections to expect: ai gets answers wrong; data privacy; we can build this on a model api ourselves; cost grows with volume.
- A proof point that lands: Results on an evaluation set built from the buyer's own history, then live results with human review switched on.
Notes for you
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.
- If you record the session, add a line at the start that says so, and link the recording and slides in the first follow-up email only if you share them.
- Confirm that you offer a conversation about the first step before you say it in the close; the script names it because it is the usual first step for this kind of business.
What this script was built from
| Setting | Value |
|---|---|
| Topic | AI agents that resolve support tickets safely |
| Duration | 45 minutes |
| Type | educational |
| Audience | Support leaders at consumer apps and SaaS companies |
| Speakers | not given |
| Product Mentions | subtle |
To sharpen this, give: speakers (it would change the introduction, which now says "your host"); key takeaways (it would change the teaching content, which now comes from the sector's own measures, objection and proof shape); your product (it would change every line that names what you sell); how you charge (it would change the commercial next step; it is conditional until you state how you charge).
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
The output gives you an Exercise preparation gate, Key Takeaways for Audience, a minute-by-minute Run of Show table, a Full Script with [ON SCREEN], SPEAKER, and [LAUNCH POLL] cues, Q&A Preparation answer patterns, and a 3-email Follow-Up Sequence (Same day, Day 3, and Day 7). The script is a timed scaffold: before going live, the host must prepare the anonymised customer example and starting records called out in Exercise preparation, and replace the [Add your link] placeholders in the follow-up emails.
What to do next
- Founder: Prepare the one real, anonymised customer example with before-and-after figures required under Exercise preparation so the middle section of the webinar shows real work.
- CEO: Check the Product mention level and closing offer to ensure the session teaches a useful method first and offers a low-friction next step (such as a pilot or working session).
- CMO: Rehearse the Run of Show timing with the speakers, set up the two poll questions in your webinar platform, and add your speakers' names and titles.
- CRO: Brief reps on the Q&A Preparation objection patterns and assign them to follow up personally with attendees who answer Poll 1 or Poll 2 with high-pain responses.
- Demand generation head: Load the 3-email Follow-Up Sequence (Same day recording link, Day 3 takeaway check-in, and Day 7 pilot offer) into your marketing automation tool with real links.
Why come back
Run the tool again when you change the webinar format (Workshop, Panel, Product demo, Fireside chat, or Customer spotlight), adjust the session length, or add named speakers and custom takeaways for a new campaign. 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 Webinar Script 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 Webinar Script 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 Webinar Script tool. topic: AI agents that resolve support tickets safely; target audience: Support leaders at consumer apps and SaaS companies; webinar type: Educational; duration: 45 minutes; product mention level: Subtle. 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 Webinar Script 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 Webinar Script.
- 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: webinar_script. REST: POST https://craft-content.gtmhelix.com/api/tools/webinar_script with this JSON body (see How it works):
{
"input": {
"topic": "AI agents that resolve support tickets safely",
"target_audience": "Support leaders at consumer apps and SaaS companies",
"webinar_type": "educational",
"duration": "45_min",
"product_mention_level": "subtle"
}
}
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