As CEO, I need to understand what my engineering team is building—but I also can’t spend hours diving into each technical improvement. When my team shipped powerful new functionality around multi-fleet agents and tools at Storytell, I needed a deep understanding fast. Here’s exactly how I used our own product to turn a quick phone call and some engineering tickets into a comprehensive understanding I could use for investor updates, technical capabilities documentation and user help docs.

CSVs and transcripts don’t usually play nice together. But with Storytell.ai, you can combine unstructured data (like audio transcripts) with structured data (like Linear tickets) to create polished, production-ready documentation. This isn’t just about saving time, it’s about scaling knowledge across your organization and specifically across teams. In this case, I used:

* A conversation transcript about new functionality we’d shipped (I used a 12-minute phone call with my engineer, Alex)

* Structured data related to that topic (I exported recent engineering tickets from Linear)

* Existing documentation we wanted to update (we have a tech stack page and help documentation)

What I did, Step-by-Step

I spent 12 minutes on the phone with Alex, having him walk me through how our new tools, agents, and skills work.

Pro tip: Storytell has the ability to directly record audio right in the project, which can be very useful to capture conversations like this.

The result: A raw, conversational transcript that contains the knowledge I needed.

I exported from Linear all the engineering tickets and issues that had activity in the last week. This gave me detailed technical context on what was actually being built and shipped.

Now I had two distinct data sources:

* Unstructured: The conversation transcript

* Structured: CSV data from Linear

I already had this tech stack page that needed updating. Here’s what I asked Storytell to do:

* Consume the existing URL (the old tech stack page)

* Using the resources above (the transcript + Linear tickets), write an executive summary about how all the technology works

* Write up detailed documentation

* Rewrite the entire page

I wrote this prompt and Storytell got to work—reasoning, doing web searches, doing knowledge base searches on my internal data. Phenomenal.

I also had this existing help documentation that needed refreshing. I asked Storytell to:

* Consume the existing webpage (I copied it as markdown)

* Run the prompt to update our help pages

I used a diff checker to see the differences between the old version and the new version.

The results were clear: There was a whole new section on the Storytell tools ecosystem that came directly from my 12-minute conversation with Alex. Storytell pulled insights from the conversation, cross-referenced with the Linear tickets, and integrated everything into cohesive documentation.

I now had three updated assets:

* Human-readable documentation with information about specialized agents, tools, skills, etc.

* LLM-optimized version — this is what we send to large language models so they can learn how Storytell works (meant to be read by machines, not humans)

* Help page updates — specific user-facing documentation about how to use these features

I sent the help page updates to our customer success team to publish.

This entire workflow—from phone call to published documentation—took less than an hour and was based on time I’d already spent on a call anyway. The magic is in combining different data types that traditionally don’t work together:

* Conversational, unstructured knowledge (transcript)

* Structured, technical detail (Linear CSV exports)

* Existing documentation (web pages)

That’s how you make use of time you’re already spending to scale knowledge across departments. Hope it’s useful!

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