customer story
Gantner
How Gantner catches the questions its docs don’t answer with GitBook
Rutva Safi
Senior Technical Writer
When Rutva Safi brought a customer question to her product manager, he had just received the same one through support. She’d spotted it in GitBook’s analytics, without anyone passing it on.
As Senior Technical Writer at Gantner, part of SALTO WECOSYSTEM, Rutva documents the software behind the electronic lockers and locks used in gyms, ski areas, amusement parks, offices and more.
When one of those customers has a question, the docs are the first place they look, and buyers checking whether the software has the feature they need often start in the API documentation. Knowing what readers ask matters, and for a long time the questions reached everyone except her.
We spoke with Rutva about how GitBook’s analytics show her what customers are asking, and have brought documentation closer to the product team.
The challenge: the last to hear what customers ask
Rutva works as a documentation team of one. Her role covers information architecture, style guides, analytics, product testing and writing. For a long time, it didn’t include hearing directly from customers.
“As a technical writer, we do not know what customers are asking or what issues they are coming up with. Sometimes they go to the support team, they go to the product managers, but they wouldn’t come to technical writers.”
In fact, useful feedback only reached Rutva if she went looking for it.
From an hour to a single click
When Rutva joined Gantner, the company had been on the same documentation platform for a long time. There was no customization of theme, and publishing meant downloading the whole document and uploading it to a server. That upload alone could take up to an hour.
“Every single change — even a single word — meant going through that whole process.”
Publishing was the most visible problem, but not the only one. Gantner had to look after hosting the content itself, readers struggled with search, and the platform offered no customer feedback or AI insights, so there was no way to see what people were looking for.
She made the case for a change, researched the options and took demos from several vendors before choosing GitBook.
“I was super convinced by GitBook and the features that it has — just one click and publish.”
A correction now goes live in seconds instead of waiting on an hour-long upload.
Seeing what customers ask
The questions Rutva had no view of on the old platform are now in front of her. Built-in AI insights shows her the questions readers ask GitBook Assistant, how many it answered, and which pages it used.
“It’s like it combines everything in a packet and gives it to you: ‘this is my analysis and this is what I’ve done,’ and now you figure out how you want to change your documentation.”
She checks it regularly, and more often around releases. Next to each question she can read the Assistant’s answer and the pages it drew on, which gives her extra context on what the reader needed.
“If anything critical that a customer asks isn’t in the document, I can see that and just quickly add it.”
Bringing questions to the product team
The content gaps feature goes a step further. It lists the questions the docs don’t answer and describes what each page is missing. When Rutva brought one of those gaps to her product manager, he told her he had just received the same question from a customer through support.
“Customers are asking, and now I am staying in the loop as a content writer. I know what they are looking for.”
Her product manager was impressed with what Rutva could see, and has asked her to demo GitBook’s analytics to his team in Austria. The reports now shape how she plans her documentation work.
“I download the report from GitBook, analyze and classify the customer questions, and plan how to optimize the docs for both humans and AI. Then I discuss priorities with stakeholders, who help decide, ‘okay, these questions are really important and urgent to respond to now.’”
New product docs, drafted from the website
Rutva has also added Gantner’s website as a connection. When Gantner launched a new product, GitBook’s AI agent drafted docs for it from the site, with a link to its source.
The draft arrived while she was in the middle of documenting another release, and Rutva was relieved that she didn’t have to split focus.
“It detected the newly added website content, understood its relevance, and suggested the entire content, including where it should go. I was like, wow. And it was mostly correct. I just had to make one or two changes. It is such a huge time saver.”
Looking ahead
Rutva is setting up regular sessions with developers and product managers to go through customer questions together. She’s also interested in GitBook’s AI translations and glossaries, which could help Gantner keep product terms consistent across its docs in several languages.
“Every day, I discover something new and think: wow, this is also there. AI insights, content gaps, broken links, style guides, and now AI agents in the editor itself.”
For Gantner, GitBook replaced an hour-long publish with a single click. It also gave its documentation team of one a regular view of what customers are asking, and a clearer idea of what to prioritize next.
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