Automating product knowledge: behind WorkWhile's AI documentation system

Documentation consistently trails behind software development. Every engineering team knows the cycle: feature delivery gets prioritized, product knowledge quickly fragments, and internal teams lose visibility. At WorkWhile, we saw this challenge clearly because we were committed to serving users who needed answers, fast.

For us, outdated documentation didn't just slow down projects, it meant our support team couldn't serve workers who rely on us for their income. Roughly 60-70% of customer support tickets stemmed not from system bugs but from gaps in accessible knowledge, including issues rooted in fragmented, outdated documentation and a lack of system visibility.

Building WorkWhile Intelligence

In the pre-AI world, teams often said, “the code is the documentation.” Now, we're building the infrastructure to make that true. During a two-day hackathon, our team developed WorkWhile Intelligence, a conversational AI interface that centralizes and distributes real-time product knowledge across our company.

At its core, this interface enables our internal teams, like support, sales, and operations , to query an up-to-date knowledge base without escalating to engineers. The AI integrates directly into existing workflows such as Slack or internal dashboards, providing accurate, immediate answers derived from up-to-date, structured documentation, historical support interactions, and, critically, from the product's actual codebase.

But the breakthrough isn't just in how the tool answers questions, it's in how the underlying knowledge stays continuously updated. Here's how our technical pipeline works:

• PR merge detection: A GitHub webhook triggers whenever a pull request merges into the main branch, gathering commit messages, PR comments, and related metadata.
• Semantic interpretation via LLMs: A large language model (LLM) then processes the code changes. Rather than relying on language-specific parsers, diff tools, or abstract syntax tree (AST), the LLM performs semantic interpretation:

  • Intent extraction: Identify the purpose behind each commit
  • Scope inference: Determine the affected product areas and features
  • Summarization: Generate human-readable summaries clearly stating what changed, why, and the impact on existing functionality
• Confidence-based documentation updates: The AI's output integrates into our knowledge base through two pathways based on the level of confidence:

  • High confidence: Documentation is auto-updated without human intervention.
  • Lower confidence: Proposed updates are pushed to Slack and automatically tag relevant product owners or engineers for rapid manual verification, ensuring continuous accuracy.

This confidence-based workflow gives us flexibility, balancing automation speed with human oversight. It is vital for maintaining trust and providing an always up-to-date, verified record of system behavior that is understandable by non-technical stakeholders.

Why we chose LLMs

We chose to lean into foundational large language models because of what they already do well, specifically their adaptability, rapid development speed, and broad language compatibility.

• Speed to production: LLMs inherently interpret diffs across multiple programming languages (Python, TypeScript, Bash), obviating the need for custom-built parsers or language-specific AST tooling
• Contextual understanding: They don't merely document syntactic changes; they contextually understand the purpose behind each code modification, a capability challenging for traditional parsers or regex-based tools

What comes next: self-healing systems

Automating documentation was our first step. But we're already pushing further. Our next ambitious technical goal is leveraging AI to proactively address actual software issues.

Currently, when bugs occur, engineers manually debug and write fixes. We envision WorkWhile Intelligence autonomously identifying these bugs, creating pull requests to resolve issues, and submitting them for engineer review. This advanced capability does not replace our engineers, but rather, frees them. Instead of triaging repetitive bugs, it allows our talent to focus more deeply on architecture, performance, and innovating for the kinds of problems that move our product forward.

Engineering for impact

Why does this technical innovation matter beyond efficiency gains? At WorkWhile, technology is never an end in itself, it's a vehicle for meaningful impact. The AI systems we build directly support blue-collar workers, a group historically underserved by the tech industry. Unlike traditional software aimed at incrementally improving productivity for white-collar roles, our technology directly influences critical outcomes: helping users keep the lights on, pay their rent, manage car payments, and pay off debt.

Real user benefits such as faster customer support resolutions, improved operational efficiency, and features like immediate wage access translate into real-world stability, ensuring a worker doesn't miss rent because of a missed shift or skip groceries because of delayed income. These are tangible outcomes, not incremental improvements. Our technical decisions are guided by urgent human realities rather than vanity metrics or abstract KPIs.

We're not incrementally improving UX, we're safeguarding livelihoods.

Our design system incorporates accessibility by default, and now, leveraging our AI tooling, we're scaling this ambition significantly. The same documentation tools now flag accessibility concerns in interfaces, suggesting fixes for confusing flows, labeling gaps, or layout issues before code ever ships. We believe that inclusive design can and should be automated, and will continuously strive to make sure that it is.

A system and a team that learns fast

Perhaps the most rewarding part of building this technology is the environment we do it in. Our remote-first, closely-knit engineering team trusts and empowers each member, working in a highly collaborative, lightweight environment where engineers, product managers, and designers continuously communicate and iterate. Hackathons like the one that spawned WorkWhile Intelligence embody our proactive problem-solving culture, and learning is fast.

Our tools evolve quickly, because the stakes are high and real. And at the end of the day, our work always returns to the human impact.

We're engineering systems that serve people whose lives are impacted by every minute of downtime or confusion, who need stability and reliability in their lives, recognizing that behind every feature is someone counting on us.

If that mission resonates with you, check out our open roles. We're hungry for curious, thoughtful engineers who want to move fast, and move people forward.

WorkWhile Careers