Q&A Recap: How Pageloop’s CEO thinks about AI-native knowledge bases

We recently hosted a live workshop on building advanced AI-native knowledge bases, featuring Nivedha Venkatesh, CEO and co-founder of Pageloop. The session focused on how to design KBs that actually work in production, ones that are structured for retrieval, stay aligned with product changes, and support both AI agents and human teams.

In the first half of the session, we covered key strategies for maintaining a strong AI-native KB:

• Sync your KB with product development
  Updates should happen when a feature hits QA, not post-launch. Strong collaboration with product teams is essential.

• Use timeless language
  Avoid using phrases like "now" or "recently" that can confuse LLMs. Documentation should be written to last.

• Audit and maintain
  Invest in a full KB audit, then maintain it with a regular review cycle tied to product updates.

• Exclude noisy content from retrieval
  Use tools like llms.txt to prevent irrelevant or outdated folders (like changelogs) from surfacing in AI responses.

• Structure for retrieval
  Articles should follow a clear, Q&A-style format, with minimal ambiguity and consistent categorization.

• Support tiered access and multi-product complexity
  Your KB should serve enterprise and SMB customers differently, while enabling clean segmentation between internal and external content.

In the second half of the workshop, we hosted a live Q&A with Nivedha, who shared real-world insights from building and scaling Pageloop’s knowledge infrastructure.

Here’s a recap of that conversation.

Why Pageloop was created

Q: What inspired you to start Pageloop, and what gap in the market were you aiming to address?

Pageloop was born out of a growing disconnect between AI systems and the knowledge they rely on. As AI gained traction in customer support, many agents started giving wrong or outdated answers because they were not grounded in accurate, structured documentation.

With over a decade of experience in support, Nivedha saw how teams were struggling to keep up. Pageloop was created to make sure that AI support tools always have access to up-to-date, high-quality content. The goal is not just better automation, but better support outcomes.

How Pageloop uses AI to maintain documentation

Q: How does Pageloop leverage AI to transform the way companies maintain their knowledge bases and support documentation?

Pageloop uses a set of specialized AI agents to manage the documentation lifecycle. These agents detect outdated content, suggest updated screenshots, rewrite articles, and even create new ones by navigating the product interface.

Unlike general-purpose tools like ChatGPT, Pageloop agents have access to full context. They understand the structure and content of the entire knowledge base, which allows them to make more accurate and consistent updates.

What makes a high-impact knowledge base

Q: Based on your experience, what are the key ingredients for creating a knowledge base that truly serves customers and support agents alike?

Nivedha emphasized three essential practices:

1. Tight collaboration with the product team
   Update the KB when features enter QA, not after they ship. This ensures support content is never out of sync.

2. Document everything
   Even small features deserve documentation. Waiting for a flood of support tickets means you are already behind.

3. Use the language of your users
   Write articles using the phrasing and terminology customers actually use. Support tickets are a goldmine for this.

Common mistakes in KB design

Q: In your opinion, what common mistakes do companies make when designing their customer support knowledge management systems?

Nivedha outlined three common pitfalls:

1. Docs are too disconnected from product and growth teams
   Documentation should live close to where product decisions are made. Otherwise, updates will always lag.

2. Lack of stakeholder reviews
   Documentation needs review from both PMs and support leads to ensure key use cases are included.

3. Poor information architecture
   A messy structure confuses users and weakens retrieval for AI. Invest early in designing a scalable structure.

Strategies for staying up to date

Q: What advice do you have for companies struggling with keeping their product documentation up to date and useful for customers?

Start by identifying the core issue. Are you short on time? Is there a communication gap with the product team?

Once you know the bottleneck, try the following:

• Use ChatGPT or similar tools to identify outdated content
• Monitor project management tools to track what features are shipping
• Export support tickets or sales call transcripts to catch new terminology
• Use note-taking tools like Grain to turn conversations into content
• Use AI to review and summarize product changes

The future of knowledge bases

Q: Looking ahead, how do you see knowledge bases evolving in the customer support space over the next 3 to 5 years?

Nivedha believes knowledge bases are only becoming more important. AI systems still need trustworthy sources, and customers still want a place to verify answers.

In the future, KBs will not just support customer service but will also serve sales, onboarding, internal ops, and product teams. The companies that succeed will treat their KB as critical infrastructure, not just a nice-to-have.

Final thoughts

This Q&A session reinforced a key message from the workshop: documentation is not just for humans. A strong knowledge base is essential for AI agents to perform well and for your support org to scale without losing quality.

Stay tuned for our next workshop, live from Las Vegas, where we will discuss how to grow your support org from Series A to Series C with confidence.