{"id":21997,"date":"2025-09-09T12:04:09","date_gmt":"2025-09-09T11:04:09","guid":{"rendered":"https:\/\/www.equalexperts.com\/?p=21997"},"modified":"2025-09-17T10:30:40","modified_gmt":"2025-09-17T09:30:40","slug":"ai-accelerated-discovery-legacy-systems","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/ai\/ai-accelerated-discovery-legacy-systems\/","title":{"rendered":"Accelerating discovery with AI: A developer\u2019s shortcut to system understanding"},"content":{"rendered":"<p>What happens when a delivery team experiments with AI to solve a real discovery challenge? At Equal Experts, we\u2019re always looking for ways to improve how we work \u2014 and that includes learning what new tools can (and can\u2019t) do in practice. In this article, we share how one team used generative AI to make sense of 40+ legacy repositories, map user journeys, and visualise system architecture \u2014 all in a matter of days. If you&#8217;re working in a complex environment, here\u2019s how AI might help you move faster and build confidence sooner.<\/p>\n<p><em>Written in collaboration with <a href=\"https:\/\/www.equalexperts.com\/blog\/our-thinking\/deep-research-for-problem-fluency-introducing-cosid-prompting\/\">Munish Malik<\/a><\/em><br \/>\n&nbsp;<\/p>\n<h1>Why we tried this: Too many repos, too little time<\/h1>\n<p>&nbsp;<br \/>\nYou\u2019ve probably been there: dropped into a complex legacy system, dozens of repositories, sparse documentation, and every question gets the same answer \u2014 \u201cwe need to investigate.\u201d<\/p>\n<p>In traditional discovery, the path forward is clear but time-consuming. Workshops, interviews, endless code reading. But when deadlines loom, the real question becomes: is there a faster way to understand the system well enough to start delivering?<\/p>\n<p>That\u2019s what our team set out to test \u2014 whether AI could accelerate the discovery phase and give us something meaningful to build on. What follows isn\u2019t a theoretical pitch. It\u2019s a practitioner\u2019s play-by-play of using LLMs, diagrams, and structured prompts to understand a tangled system faster, without cutting corners.<br \/>\n&nbsp;<\/p>\n<h1>The AI-accelerated discovery methodology<\/h1>\n<p>&nbsp;<\/p>\n<h2>Step 1: Repository-level AI analysis<\/h2>\n<p>We started with over 40 repositories across two systems. Instead of wading through them manually, we pointed LLMs at the source and asked for:<\/p>\n<ul>\n<li aria-level=\"1\">A business overview of each service<\/li>\n<li aria-level=\"1\">A technical summary of what it does<\/li>\n<li aria-level=\"1\">How it validates or modifies data<\/li>\n<li aria-level=\"1\">Key algorithms and workflows<\/li>\n<li aria-level=\"1\">Data model documentation<\/li>\n<li aria-level=\"1\">Entity relationships<\/li>\n<li aria-level=\"1\">API contract breakdowns<\/li>\n<\/ul>\n<p>We used structured prompting techniques to generate these outputs consistently, then used docsify to format it all into a searchable knowledge base. So if someone wanted to understand where a certain business logic lived, they could search for the term and land on the right repo instantly.<\/p>\n<p><strong>Result:<\/strong> A fast, searchable documentation base for the entire codebase.<br \/>\n&nbsp;<\/p>\n<h2>Step 2: Journey mapping across real scenarios<\/h2>\n<p>Once we had service-level visibility, we pushed further: tracing actual business scenarios end-to-end. With AI\u2019s help and a few prompt iterations, we built journey maps that integrated:<\/p>\n<ul>\n<li aria-level=\"1\">Customer-facing experiences<\/li>\n<li aria-level=\"1\">System-level processes<\/li>\n<li aria-level=\"1\">Component interactions<\/li>\n<li aria-level=\"1\">Sequence diagrams<\/li>\n<li aria-level=\"1\">A working domain glossary<\/li>\n<\/ul>\n<p>We could now see how customer interactions flowed through the system, how various business rules were applied, and how that event was ultimately processed. Not only did this make system behaviour visible, but it also helped us quickly build a shared understanding of the domain.<\/p>\n<p><strong>Result:<\/strong> Comprehensive journey maps across complex business cases.<br \/>\n&nbsp;<\/p>\n<h2>Step 3: Architecture diagrams, powered by prompt and polish<\/h2>\n<p>For architecture, we paired AI-generated system insights with existing team artefacts \u2014 mainly Miro boards \u2014 and generated C4 diagrams, which gave us C4 views showing boundaries, responsibilities, and system interactions in a way that non-devs could navigate, and engineers could drill into.<\/p>\n<p><strong>Result:<\/strong> Clean architecture diagrams that made onboarding and reasoning about the system easier.<br \/>\n&nbsp;<\/p>\n<h2>Step 4: Drilling down to use cases, powered by the resynthesizing process<\/h2>\n<p>With a high-level view established, we shifted focus to specific business use cases. Here, the true power of our AI-generated artefacts shone through.<\/p>\n<p>We resynthesised the architecture diagrams, data models, and journey maps to rapidly build context around critical business flows.<\/p>\n<p>This layered approach was a game-changer, allowing us to quickly understand specific interactions, data transformations, and system behaviours relevant to particular scenarios.<\/p>\n<p><strong>Result:<\/strong> We were able to move beyond general system understanding and pinpoint exactly how individual business concerns were handled<br \/>\n&nbsp;<\/p>\n<h1>Tools and Models Used<\/h1>\n<p><em>Here\u2019s what powered our AI-accelerated discovery process:<\/em><\/p>\n<h2><strong>Tools<\/strong><\/h2>\n<p>Cursor, Claude Code, Claude Desktop<\/p>\n<h2>Libraries &amp; Frameworks<\/h2>\n<p>Docsify, likeC4, PocketFlow<\/p>\n<h2>Models<\/h2>\n<p>Claude Opus 4.1, Claude Sonnet 4, Claude Sonnet 3.7<\/p>\n<p>These choices weren\u2019t about finding the \u201cbest\u201d tool \u2014 they were what worked well for this context. Our focus was always on reducing discovery time while building meaningful, usable outputs for the team.<\/p>\n<p>&nbsp;<\/p>\n<h1>A tool we didn\u2019t expect to love \u2014 but did<\/h1>\n<p>We tested <a href=\"https:\/\/github.com\/The-Pocket\/PocketFlow-Tutorial-Codebase-Knowledge\">PocketFlow<\/a>, which turned out to be surprisingly effective at generating \u201chow-to\u201d style documentation. Think of it as AI turning code into developer guides. Instead of just summarising what a module does, it explained:<\/p>\n<ul>\n<li aria-level=\"1\">How authentication is implemented<\/li>\n<li aria-level=\"1\">What happens during payment processing<\/li>\n<li aria-level=\"1\">Step-by-step flows, complete with code snippets<\/li>\n<\/ul>\n<p>This proved incredibly useful for onboarding \u2014 faster than dry documentation and closer to real \u201cdeveloper onboarding\u201d guides.<br \/>\n&nbsp;<\/p>\n<h1>Reflections: What worked and how our role has changed<\/h1>\n<p>&nbsp;<\/p>\n<h2>What worked well:<\/h2>\n<ul>\n<li aria-level=\"1\">Reusing and layering AI outputs (e.g. feeding repo summaries into journey mapping prompts)<\/li>\n<li aria-level=\"1\">Rapid prompt iteration \u2014 fast feedback cycles let us refine outputs in minutes, not days<\/li>\n<li aria-level=\"1\">Using journey maps as a scaffold to discuss with domain experts<\/li>\n<li aria-level=\"1\">We worked in a &#8220;mobbing&#8221; style &#8211; product and engineering together daily, making decisions in real-time with minimal handoffs<\/li>\n<\/ul>\n<h2><\/h2>\n<h2>The human element still matters<\/h2>\n<p>LLMs read the code. Humans read between the lines. We had to contextualise what was AI-generated:<\/p>\n<ul>\n<li aria-level=\"1\">What patterns were deliberate vs. technical debt<\/li>\n<li aria-level=\"1\">Which oddities had business logic behind them<\/li>\n<li aria-level=\"1\">Which terms were outdated or misaligned with current thinking<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h1>Final thoughts: Try it, refine it, share it<\/h1>\n<p>&nbsp;<br \/>\nThis wasn\u2019t about skipping the hard work of understanding a system. It was about doing the groundwork faster, so we could ask better questions sooner. For us, using AI in discovery meant moving from \u201cwe\u2019ll get back to you\u201d to \u201chere\u2019s what we think is happening \u2014 can you confirm?\u201d in days, not weeks.<\/p>\n<p>If you\u2019re about to start discovery in a messy system, this approach is worth trying. Start small \u2014 pick a repo, write a prompt, see what comes back. Then layer in journey mapping and diagramming as your confidence grows.<\/p>\n<p>If you\u2019ve been experimenting with AI in delivery, we\u2019d love to hear about it. Or if you\u2019re facing the uphill battle of legacy system discovery and want to chat approaches, get in touch. We\u2019re always up for swapping war stories, prompts, or diagramming tricks.<\/p>\n<h3>Disclaimer<\/h3>\n<p>Equal Experts is not affiliated with or commercially connected to any of the tools mentioned in this post. We\u2019re sharing this approach purely to demonstrate how we experimented with AI tooling to accelerate delivery in a real-world context. Your mileage may vary \u2014 and that\u2019s part of the fun.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>What happens when a delivery team experiments with AI to solve a real discovery challenge? At Equal Experts, we\u2019re always looking for ways to improve how we work \u2014 and that includes learning what new tools can (and can\u2019t) do in practice. In this article, we share how one team used generative AI to make [&hellip;]<\/p>\n","protected":false},"author":290,"featured_media":21998,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[412,917,5],"tags":[99,306,929,426,701,873,928],"location":[],"class_list":["post-21997","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-experiment-with-us","category-our-thinking","tag-ai","tag-architecture","tag-delivery-acceleration","tag-generative-ai","tag-legacy-systems","tag-prompt-engineering","tag-software-discovery"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Accelerating Discovery with AI in Legacy Systems | Equal Experts<\/title>\n<meta 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