Most product and CX leaders would be uncomfortable learning that 40% of their support costs could be eliminated with a well-maintained knowledge base.
This figure, identified in Desku.io's 2023 research , highlights a significant opportunity that most SaaS companies miss while remaining trapped in never-ending cycles of outdated documentation, frustrated users, and overworked support teams.
The reality is even more concerning when examining knowledge base accuracy. A survey of 224 contact center professionals by CallCentreHelper.com revealed that only 19.1% rated their knowledge base information as "very accurate," while 43.1% considered it "reasonably accurate." This indicates that over 80% of knowledge bases aren't meeting optimal accuracy standards. Additionally, 13.8% reported having "inconsistent" information standards, and 0.6% admitted their information was "not at all accurate."
The Real Cost of Maintaining Documentation When we talk to product and support leaders, we consistently hear the same story: maintaining help documentation is a constant, resource-draining struggle.
According to SaaS Capital's 2023 benchmark report , SaaS companies allocate a median of 8.5% of their Annual Recurring Revenue to customer support and success functions, which includes knowledge base maintenance. This translates to significant expenditure, with BusinessPlan-Templates reporting that companies typically spend between $50,000 and $500,000 annually on software development and maintenance, including documentation.
Plus, the time investment is enough to drain employees. A 2023 discussion on Reddit's r/ProductManagement revealed that some product managers spend up to 5% of their workweek just maintaining help docs, even with a dedicated technical writer on staff. This aligns with findings from VentionTeams , which notes that up to 60% of software maintenance time is spent on enhancements and updates, with documentation updates consuming a significant portion of that time.
What makes this particularly frustrating is that despite all this investment, knowledge bases remain notoriously inaccurate.
While specific percentages regarding software product knowledge bases are limited, the CallCentreHelper.com survey suggests that a considerable number of organizations struggle to maintain fully accurate and up-to-date knowledge repositories. This accuracy gap represents additional hidden costs in the form of repeated support inquiries, customer confusion, and internal inefficiencies.
Why Traditional Knowledge Bases Fail The psychology behind knowledge base failures is straightforward. Traditional help doc maintenance approaches suffer from a slew of problems.
First, they're constantly outdated. Your product evolves daily, but documentation updates require manual effort. Features change, workflows evolve, and new use cases emerge, but your documentation stays frozen in time, creating a growing disconnect between help content and actual product functionality.Second, they create what cognitive scientists refer to as "context breaks." When users need to leave your product to find help, they're pulled out of their flow state and forced to translate their problem into search terms. This cognitive shift not only frustrates users but decreases the likelihood they'll successfully find solutions.Third, most knowledge bases are built for documentation teams, not users. They organize content based on product structure rather than how users actually think about or encounter problems, making information harder to discover when needed most.The accuracy problem compounds these issues. With over 80% of knowledge bases falling short of optimal accuracy according to the CallCentreHelper.com survey, users learn to distrust documentation. Each inaccurate article or outdated screenshot reinforces the perception that self-service is unreliable, driving more users toward direct support channels and creating a vicious cycle of documentation debt.
The business impact is significant. According to Desku.io's 2023 research, a poorly maintained knowledge base leads to a 23% increase in customer support tickets. Support teams consequently get stuck handling repetitive queries that could be self-served, reducing their ability to handle complex issues that truly need human attention. When combined with the accuracy issues identified by CallCentreHelper.com, where less than one-fifth of organizations consider their knowledge base "very accurate," the magnitude of this documentation debt becomes clear.
What Your Best Support Person Actually Does Your most experienced support team members deliver exceptional service through more than just product knowledge. They intuitively understand context without requiring detailed explanations from users. Their pattern recognition across similar issues streamlines troubleshooting. They instinctively know when to provide guidance during a user's journey. Their continuous learning and adaptation keeps pace with product evolution.
Documentation systems traditionally fail to replicate these human capabilities, creating a persistent gap between support quality and self-service effectiveness.
Introducing Brainfish’s Self-Generating Knowledge Base Knowledge base technology that observes actual user interactions would fundamentally transform support experiences. Systems that automatically generate and update documentation based on real usage patterns would eliminate maintenance burdens. Documentation that appears contextually precisely when users need assistance would dramatically improve satisfaction metrics.
We’ve built this with our self-generating knowledge base powered by ambient AI . This represents a fundamental shift from static documentation to dynamic, learning-based support systems.
Unlike traditional documentation that requires constant maintenance, our approach uses computer vision and machine learning to understand your product from the user's perspective. The system observes how people interact with your application, identifies common patterns and pain points, and automatically generates relevant, contextual help content that evolves alongside your product.
How It Works: The Technical Magic Behind Self-Generating Documentation Brainfish's ambient AI agent replicates your most attentive support staff through continuous observation and learning processes.
Observation forms the foundation of the system. Computer vision technology actively monitors user interactions within your product interface, building contextual understanding of user intent through actual behavior. This generates a comprehensive understanding of natural navigation patterns that documentation teams typically cannot capture.
Pattern recognition analyzes thousands of user sessions while maintaining enterprise-grade security and privacy protocols. The system identifies common workflows, friction points, and successful user patterns to construct a knowledge graph representing your product from the user's perspective rather than following predetermined documentation structures.
The automatic content generation process leverages these insights to create clear, contextual documentation that addresses real user needs, not just what your team thinks users might need. This content evolves organically as usage patterns change. We’re talking about how-to guides with the most helpful and relevant text and images.
Perhaps most valuable is the contextual delivery mechanism. Help appears exactly when and where users need it, eliminating the frustrating search process and keeping users in their workflow. This maintains cognitive continuity and dramatically reduces user frustration.
Finally, continuous learning ensures the system evolves with your product, automatically updating documentation as features change and users discover new ways of working. This creates a virtuous cycle of improvement without manual intervention.
Implementation takes minutes, not months. While traditional documentation solutions demand weeks of setup and constant maintenance, Brainfish starts learning your product immediately with just a few lines of code, as confirmed by multiple customer implementation studies.
The ROI of Self-Generating Documentation Customer implementations demonstrate compelling business impact across multiple metrics. Smokeball, a legal practice management software company, achieved an 83% self-service rate while maintaining a frictionless user experience. Support NPS scores jumped 17 points (from 60 to 77) for another enterprise client following implementation. Training efficiency improved substantially for a third customer, with new user onboarding time decreasing by 30% through contextual documentation.
Desku.io's 2023 industry research confirms these outcomes aren't outliers. Their analysis shows well-maintained knowledge bases consistently reduce support tickets by 23% while decreasing overall support costs by 40%.
The accuracy improvements are equally significant. While traditional knowledge bases struggle with accuracy issues—over 80% falling short of "very accurate" according to CallCentreHelper.com—self-generating systems continuously update based on actual product state and usage patterns. This eliminates the accuracy gap that plagues manual documentation processes, helping rebuild user trust in self-service options.
Cost reduction represents just one dimension of improvement. Users consistently find answers within their natural workflow, maintaining cognitive continuity and dramatically reducing measured customer effort scores. Product teams observe deeper feature adoption and usage patterns when documentation appears contextually. Support organizations redirect resources toward complex, high-value interactions instead of answering repetitive questions. The continuous collection of usage data generates insights for product enhancement, establishing ongoing improvement cycles without additional investment.
Beyond Traditional Documentation Tools The self-generating knowledge base isn't just a better way to create documentation. It represents a fundamentally different approach to user assistance that brings together multiple functions traditionally handled by separate tools.
This technology supplements and enhances traditional knowledge bases like Zendesk, Help Scout, and Confluence by automatically creating and updating content without manual intervention. It enriches product analytics tools by adding contextual understanding to raw user behavior patterns. Most importantly, it augments existing support workflows by providing instant, contextual assistance at the moment of need.
For many organizations, this approach can replace or reduce reliance on manual documentation tools that consume significant resources. It offers a superior alternative to basic chatbots that often create friction rather than reducing it. The technology can also supplant standalone product tours and onboarding tools while consolidating multiple disconnected help content repositories.
The technology functions as an intelligent augmentation layer across existing systems, continuously learning from interactions and enhancing performance across integrated components. Organizations maintain their current technology investments while gaining significant improvements through contextual intelligence that connects previously siloed information sources.
The Future of Product Support Brainfish developed this solution to eliminate barriers between users and help content while making products fundamentally easier to use. Most chatbot implementations create frustrating experiences disconnected from actual user contexts and needs.
Products that continuously learn and adapt to user behavior patterns will dominate the future software landscape. These systems provide assistance naturally and precisely when needed without disrupting workflows. Modern video games already implement similar adaptive learning to accommodate individual play styles in real-time. Brainfish's ambient AI agent applies this same principle to business applications, continuously observing and adapting to actual product usage patterns.
This approach eliminates outdated documentation and manual updates while resolving the fundamental disconnect between how products work and how they're documented. The result is a living knowledge base that evolves naturally with your product and responds to genuine user needs as they emerge—one that maintains accuracy automatically without requiring constant human oversight.
Want to see how a self-generating knowledge base could transform your product experience? Schedule a demo or check out our Product Hunt launch for more details.
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