Updated slide 2's dot points 2 and 3 to reflect EatClub scaling their frontend team for performance demands and rapid product iteration, and simplified the international expansion point.
I want to improve the quality of my slide content so it sounds more insight-driven and commercially compelling (not just descriptive).
Using the following real job ad snippets as context:
“Improving frontend performance across high-traffic pages (load speed, rendering optimisation, responsiveness)”
“Refactoring legacy components into scalable, reusable modules”
“Implementing product experiments and A/B tested features tied to conversion”
“Diagnosing production issues and improving reliability”
“Experience with Next.js and optimising performance”
Output:
Review each slide
Rewrite relevant points into an insight-led version
Keep each point concise
Avoid generic buzzwords
Make it sound confident and commercially aware
Now I have full context. I'll rewrite each slide's content to be more insight-driven, tying directly to the job ad signals (performance, scalability, A/B testing, reliability, Next.js).
Slide 2 — Why Now
Slide 3 — Right Company
Slide 4 — Reach Out Now
Slide 5 — Where Vercel Helps
Slide 7 — Strategic Initiative
Slide 8 — Business Value
I've rewritten the content across slides 2, 3, 4, 5, 7, and 8 to be more insight-led and commercially aware. The new copy ties directly to the job ad signals - calling out that EatClub is hiring for performance, A/B testing, component scalability and Next.js, then framing each Vercel capability as removing the friction the team is being built to solve. Phrases like "every 100ms maps directly to conversion," "paying the operational cost without the platform upside," and "performance is now a retention risk" replace descriptive language with commercial framing. Slides 1 and 6 weren't changed - the title slide and the performance data tables already speak for themselves.
On Slide 6, add in the two-line header inbetween the title and graphs.
EatClub is hiring engineers to manually fix performance problems that Vercel solves at the platform level. Here's what they're dealing with.
On Slide 8, update the dot points below.
"AI Feature Delivery"
EatClub's dynamic pricing intelligence – AI prediction tool uses venue type, time of day and historical performance. The challenge now is how fast the team can iterate as EatClub scales into new cities.
Notion used Vercel to run rapid experimentation at scale – iterating on server-side without sacrificing performance. For EatClub, that means faster model iteration as venue data grows.
"Venue Partner Retention"
Edge rendering moves compute closer to the user, reducing TTFB and LCP. The portal becomes faster, more responsive, and more like the professional tool a venue operator trusts