Case studies — real teams shipping with AI and no-code
Case studies in this section unpack what actually happened when a team rolled AI or no-code into a workflow: which tools were chosen, what the build looked like, what broke, and the measurable result. Numbers before/after, time spent, money saved or earned — not "AI transformed our business" testimonials.
What's in a case study
Standard structure: company / team context (size, industry, prior stack), the problem (with the cost of not solving it), tool selection rationale (why this AI / no-code tool over alternatives), the build (architecture, integration points, edge cases), launch and metrics (real before/after numbers), and the after-six-months retrospective — what aged well, what they'd do differently.
Cases cover both the success path and the mess. A "we shipped an internal RAG and saw a 40% drop in support tickets" case sits next to a "we tried building a sales agent on Claude and pulled the plug after two months — here's why" piece. Both are equally useful for builders deciding whether to invest.
Verification
Where possible, cases include direct quotes from the team that shipped them, screenshots from real dashboards, and links to the live product. Anonymised numbers are flagged — we never invent metrics. If a number can't be verified, it gets called out explicitly.
Why this matters
Case studies fill the gap between marketing claims and your own pilot. Reading what worked (and failed) for similar-sized teams shortcuts months of trial-and-error. Use the category as a research stage before committing to a tool — most case studies link to the matching Academy course or expert in the catalog if you decide to move forward.