How I Would Fix slow pages and weak Core Web Vitals in a Make.com and Airtable AI chatbot product Using Launch Ready.

How I Would Fix slow pages and weak Core Web Vitals in a Make.com and Airtable AI chatbot product Using Launch Ready

The symptom is usually the same: the chatbot "works", but the page feels heavy, the first load is slow, and Core Web Vitals are failing. In practice, I usually find one of two things: too much client-side work on the landing page, or the chatbot flow waiting on Make.com and Airtable in a way that blocks rendering.

The first thing I would inspect is the actual user path from page load to first useful interaction. I want to see what is delaying LCP, what is causing layout shift, and whether the app is shipping secrets, extra scripts, or unbounded third-party calls into the browser.

Triage in the First Hour

1. Open the live site in Chrome DevTools and run Lighthouse on mobile.

• Capture LCP, CLS, INP, total blocking time, and unused JavaScript.
• If LCP is over 2.5s or CLS is above 0.1, treat it as a launch risk.

2. Check real-user data if you have it.

• Look at GA4, PostHog, Sentry Performance, Vercel Analytics, or Cloudflare Web Analytics.
• Compare lab results to field data. If field LCP is worse by 30 percent or more, there is likely a network or backend delay.

3. Inspect the Network tab for the first page load.

• Identify large JS bundles, slow fonts, unoptimized images, and third-party tags.
• Check whether Make.com webhooks are being called during initial render.

4. Review the browser console and server logs.

• Look for repeated retries, failed API calls, CORS errors, and long-running requests.
• Confirm whether Airtable requests are being made directly from the client.

5. Check deployment settings and environment variables.

• Verify production env vars exist and no secrets are exposed in frontend code.
• Confirm Cloudflare caching rules are not bypassing static assets.

6. Open Make.com scenarios.

• Find any scenario that triggers on page load or chat open.
• Check for loops, unnecessary modules, duplicate Airtable lookups, or slow HTTP steps.

7. Inspect Airtable base design.

• Review table size, formula complexity, linked record depth, and attachment usage.
• Slow bases often mean slow reads.

8. Confirm bot architecture.

• Determine whether the chatbot UI waits for Airtable data before rendering.
• If yes, that is a direct UX and performance issue.

Root Causes

| Likely cause | What it looks like | How I confirm it | |---|---|---| | Client-side overwork | Slow LCP, high INP, large JS bundle | Bundle analyzer shows heavy vendor code or chatbot SDKs | | Direct Airtable calls from browser | Slow responses and exposed API keys risk | Network tab shows Airtable endpoints in frontend requests | | Make.com scenario latency | Chat replies arrive late or time out | Scenario execution history shows long step durations | | Airtable base inefficiency | Reads get slower as records grow | Base with many formulas, attachments, and linked records | | Too many third-party scripts | Main thread blocked before content appears | Lighthouse flags long tasks and script weight | | Weak caching and no CDN tuning | Repeat visits still feel slow | Static assets miss cache headers or bypass Cloudflare |

A key security issue here is often hidden inside performance work: founders sometimes put Airtable keys or webhook secrets in browser code just to make things "work". That creates exposure risk for customer data and makes abuse easier if someone inspects network traffic.

The Fix Plan

npm run build && npm run analyze

1. Separate "page render" from "chat data fetch".

• The page should render immediately with placeholder content.
• Load chatbot state after first paint instead of blocking it.

2. Move sensitive logic server-side.

• Do not call Airtable directly from the browser if it exposes credentials or large datasets.
• Put a thin backend endpoint in front of Airtable and Make.com so you can validate input, rate limit requests, and cache safe responses.

3. Reduce what loads on first view.

• Remove unused libraries from the chatbot widget.
• Delay non-critical analytics pixels until after interaction or consent.

4. Add caching at the edge where possible.

• Cache static assets through Cloudflare with long TTLs.
• Cache safe read-only responses for common chatbot configuration data for 30 to 300 seconds.

5. Optimize images and fonts.

• Convert hero images to WebP or AVIF.
• Use system fonts or preload only one font family with limited weights.

6. Simplify Airtable access patterns.

• Replace broad table scans with filtered views or narrower queries where possible.
• Reduce formula chains that recalculate on every change.

7. Harden Make.com scenarios.

• Split one big scenario into smaller steps if one failure causes retries across everything.
• Add error routes so transient failures do not create duplicate messages or repeated writes.

8. Protect against abuse while improving speed.

• Add rate limits per IP or session on chat submit endpoints.
• Validate payload size so someone cannot flood your bot with oversized prompts.

9. Set up monitoring before redeploying.

• Track page speed metrics plus scenario failures in one place if possible.
• Alert when p95 response time crosses 2 seconds for chat replies or when uptime drops below 99.9 percent.

10. Use Launch Ready to package deployment safely.

• In 48 hours I would handle DNS, redirects, subdomains, Cloudflare setup, SSL, caching rules, DDoS protection, SPF/DKIM/DMARC if email is involved,

production deployment, env vars, secrets handling, uptime monitoring, and a handover checklist for your team.

My rule here is simple: fix architecture first, then polish UI second. If you try to "design" your way out of a slow request chain without removing blocking work from the browser, you usually waste another week and still fail Core Web Vitals.

Regression Tests Before Redeploy

1. Run Lighthouse on mobile again after changes.

• Target: LCP under 2.5s on a mid-tier device profile.
• Target: CLS under 0.1 and INP under 200ms where possible.

2. Test first load on a throttled connection.

• Use Fast 3G or equivalent simulation.
• The homepage should show usable content within 2 seconds visually even if chat data loads later.

3. Verify chatbot behavior end-to-end.

• Send a normal prompt

, an empty prompt, , a very long prompt, , and a malformed input payload.

• Confirm no crashes,

no duplicate sends, no broken loading states, and no infinite spinners.

4. Check security controls before shipping after any backend change:

• Secrets are not present in client bundles

. - CORS allows only approved origins . - Rate limiting works . - Logs do not store raw sensitive prompts unless explicitly required .

5. Validate Make.com scenarios with test records only before live traffic: - One successful run . - One simulated failure path . - One retry case . - One duplicate submission check .

6. Compare before-and-after metrics: - Lighthouse score target above 85 on mobile . - JS bundle reduction of at least 20 percent if possible . - Chat response p95 under 2 seconds for cached paths . - Zero critical console errors .

7. Do one manual QA pass on mobile Safari and Chrome Android because many founders only test desktop Chrome and miss layout shifts, sticky footer bugs, and delayed button taps there.

Prevention

I would put guardrails around both performance and security so this does not come back in two weeks.

• Add performance budgets in CI:

set limits for bundle size, image weight, number of third-party scripts, and Lighthouse thresholds before merge.

• Require code review for any change touching auth,

webhook handling, environment variables, CORS, logging, or external integrations.

• Keep secrets out of frontend code:

use server-side env vars only, rotate keys quarterly, and remove stale Make.com connections that nobody owns anymore.

• Monitor real-user metrics:

track LCP, CLS, INP, p95 chat latency, scenario failures, error rates, uptime, and abandoned sessions after bot open.

• Improve UX around loading states:

show skeletons instead of blank screens, avoid layout jumps when chat opens, and make error messages clear enough that users know whether to retry or refresh.

• Review Airtable structure monthly:

archive old records, stabilize formulas, remove unused views, and split large bases before they become operational drag.

• Red team the bot flow defensively:

test prompt injection attempts that try to force data disclosure, tool misuse, or unsafe instructions; ensure any external action requires validation or human escalation when needed.

When to Use Launch Ready

Use Launch Ready when you already have a working AI chatbot product but it is not production-safe yet because launch plumbing is messy or missing. This sprint fits best when you need domain setup,

email deliverability,

Cloudflare,

SSL,

deployment,

secrets,

and monitoring handled fast without turning it into a multi-week rebuild.

I would get your product into a state where users can actually trust it:

• DNS configured correctly
• redirects cleaned up
• subdomains wired properly
• Cloudflare protecting traffic
• SSL active everywhere
• caching tuned for static assets
• DDoS protection enabled
• SPF/DKIM/DMARC set up if email sending matters
• production deployment completed
• environment variables moved out of public code
• secrets stored safely
• uptime monitoring added
• handover checklist delivered

What you should prepare:

• domain registrar access
• hosting access such as Vercel,

Netlify, or similar

• Cloudflare access if already connected
• Make.com account access
• Airtable base access with admin rights if possible
• list of current integrations plus any API keys that need rotation
• screenshots or Loom video showing where the slowdown happens

If your founder goal is "get this live without breaking onboarding," this is exactly where I would step in instead of letting another week disappear into trial-and-error fixes.

Delivery Map

References

1. https://roadmap.sh/frontend-performance-best-practices 2. https://roadmap.sh/backend-performance-best-practices 3. https://roadmap.sh/api-security-best-practices 4. https://roadmap.sh/cyber-security 5. https://developers.cloudflare.com/cache/

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Take the next step

If this is a problem in your product right now, here is what to do next:

• [Use the free Cyprian tools](/tools) - estimate cost, score app risk, check launch readiness, or pick the right service sprint.

• [Book a discovery call](/contact) - I will tell you honestly whether you need a sprint or if you can DIY the next step.

*Written by Cyprian Tinashe Aarons - senior full-stack and AI engineer helping founders rescue, launch, automate, and scale AI-built products.*