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 obvious: the chatbot page feels sticky, the first response takes too long, and Core Web Vitals are red or borderline. In a Make.com and Airtable stack, the most likely root cause is not "the AI" itself, but too much work happening before the user sees anything useful: heavy frontend bundles, blocking scripts, slow third-party calls, and chat requests waiting on multiple automation steps.

The first thing I would inspect is the full request path from browser load to first chat reply. I want to know whether the delay is in the frontend, Cloudflare, your app server, Make.com scenarios, Airtable reads/writes, or an LLM call that is being chained through too many steps.

Triage in the First Hour

1. Open Chrome DevTools and record:

• Lighthouse report
• Performance tab
• Network waterfall
• Console errors
• Largest Contentful Paint element

2. Check the live page on mobile and desktop:

• First contentful paint
• LCP element size and source
• CLS shifts during load
• INP on initial interaction with the chatbot

3. Inspect hosting and delivery:

• Cloudflare dashboard
• DNS records
• Cache rules
• Page Rules or Transform Rules
• SSL status
• WAF events

4. Review app build output:

• bundle size
• third-party scripts
• image sizes
• fonts
• hydration warnings if this is React-based

5. Audit Make.com:

• scenario run history
• execution time per module
• retries and failed runs
• webhook latency
• any loops or filters causing extra calls

6. Audit Airtable:

• base size
• view filters
• formula complexity
• linked record depth
• attachment fields used on the critical path

7. Check API security basics while you are there:

• exposed keys in client code
• webhook authentication
• rate limits on public endpoints
• CORS settings
• logging of secrets or user prompts

8. Confirm monitoring:

• uptime checks
• error tracking
• synthetic tests for chat send and response flow

A simple diagnostic command I would run against the live page:

curl -I https://yourdomain.com/chat && curl https://yourdomain.com/chat | head

That tells me whether headers, cache behavior, redirects, or HTML payload size are already hurting performance before JavaScript even loads.

Root Causes

| Likely cause | What it looks like | How I confirm it | | --- | --- | --- | | Frontend bundle too large | Slow first paint, poor mobile LCP | Lighthouse shows high JS transfer size and long main-thread blocking | | Too many third-party scripts | INP gets worse, layout shifts appear | Network waterfall shows analytics, widgets, or trackers delaying render | | Make.com chain is too long | Chat reply takes 5 to 20 seconds | Scenario history shows multiple sequential modules and retries | | Airtable used as a live database for every request | Slow lookups and occasional timeouts | Airtable requests dominate latency and spike under load | | No caching or edge delivery | Every visit hits origin and automation tools again | Cloudflare cache miss rate is high and repeat visits are not faster | | Weak API security controls | Abuse, spam traffic, or prompt injection increases load | Logs show repeated anonymous requests or malformed payloads |

1. Frontend bundle bloat

This is common when founders ship fast with no performance budget. A chatbot page only needs a small shell, a message composer, a conversation list, and minimal supporting assets.

I confirm this by checking bundle analyzer output, script count, and whether non-critical code loads before the first meaningful paint.

2. Third-party script overload

Chat widgets often end up with analytics tags, heatmaps, CRM snippets, cookie banners, scheduling widgets, and multiple pixels all firing at once. That hurts LCP and INP more than most founders expect.

I confirm this by disabling scripts one by one in staging and comparing Lighthouse scores.

3. Over-automation in Make.com

A lot of AI products become slow because each user message triggers a long scenario: validate input, fetch Airtable records, enrich data, call OpenAI or another model provider, write back to Airtable, notify Slack, update CRM, then return response.

I confirm this by timing each module in scenario logs and looking for unnecessary serial steps that can be parallelized or removed.

4. Airtable as the hot path datastore

Airtable is fine for admin workflows and light operational data. It is not ideal as a high-frequency runtime database for every chatbot turn if you need low latency.

I confirm this by measuring query time on views with formulas, linked records, attachments, or large tables.

5. Missing edge caching and static delivery

If your marketing page or app shell is rebuilt on every request instead of cached at the edge, users pay for it on every visit.

I confirm this by checking Cloudflare cache status headers and comparing cold versus warm loads.

6. Weak API security causing noise traffic

If endpoints are public without rate limiting or basic request validation, bots can create extra load that looks like "slow pages" but is really avoidable abuse.

I confirm this by reviewing logs for repeated IPs, invalid payloads, missing auth headers, or prompt injection attempts hitting automation endpoints.

The Fix Plan

My recommendation is to fix this in layers: browser first, then edge delivery, then automation path length. That gives you visible speed gains without breaking the chatbot logic.

1. Split the product into two paths:

• marketing pages: static-first and aggressively cached
• chatbot runtime: minimal API surface with strict validation

2. Reduce frontend weight:

• remove unused libraries
• defer non-critical scripts until after interaction
• lazy-load chat history panels and settings screens
• compress images to modern formats like WebP or AVIF where relevant

3. Improve rendering strategy:

• server-render or pre-render landing content if possible
• keep above-the-fold content small and stable
• reserve space for chat components to reduce CLS

4. Move slow work out of the critical path:

• do not wait on Airtable writes before showing user feedback
• return an immediate "working on it" state within 300 ms if possible
• process enrichment tasks asynchronously when they are not required for first response

5. Simplify Make.com scenarios:

User message -> validate -> respond fast -> queue enrichment -> write audit log -> sync Airtable later

This pattern keeps perceived latency down because the user gets a response quickly while background tasks finish safely.

6. Use Airtable only where it fits:

• admin dashboards

operations tables content management low-volume reference data

For hot-path runtime state like recent chat turns or session metadata under load p95 should be kept under 500 ms outside Airtable if possible.

7. Add Cloudflare controls:

• cache static assets at edge

enable compression set proper TTLs protect forms and APIs with WAF rules block obvious bot abuse enforce HTTPS everywhere

8. Tighten API security:

• require signed webhooks where supported
• validate all inbound fields server-side
• reject oversized payloads early
• store secrets only in environment variables or secret managers.
• never expose OpenAI keys or Make webhook URLs in client-side code

9. Add observability before redeploying:

• track LCP/CLS/INP in real user monitoring.
• log request duration per route.
• tag Make.com runs by scenario name.
• alert on failure spikes above 3 percent.

10. If the stack still feels heavy after cleanup:

• keep marketing site on static hosting.
• move chatbot API behind a thin backend layer.
• use Airtable as a back office tool instead of your main runtime datastore.

That is usually the cleanest path because it lowers support load without forcing a full rebuild.

Regression Tests Before Redeploy

Before I ship anything back to production I want proof that speed improved without breaking chat behavior.

1. Performance checks:

• Lighthouse mobile score at least 85 for performance.
• LCP under 2.5 seconds on average broadband.
• CLS under 0.1.
• INP under 200 ms for basic interactions.

2. Functional checks:

• chat sends messages successfully.
• empty states render correctly.
• loading state appears within 300 ms.
• error state appears when Make.com fails.
• retry button works once without duplicating messages.

3. Security checks:

• invalid inputs are rejected cleanly.
• webhook endpoints require expected auth or signature controls.
• secrets do not appear in browser source maps or console logs.
• rate limiting blocks repeated spam submissions.

4. Integration checks:

• Airtable writes happen exactly once per message.
• Make.com retries do not duplicate records.
• failed automations are logged with enough context to debug safely.

5. Edge-case checks:

• slow network throttling at "Fast 3G"
• offline refresh behavior
• long chat transcripts
• emoji and non-Latin text
• oversized prompts

6. Acceptance criteria I would use:

• first usable screen appears in under 2 seconds on mobile median devices.
• chatbot reply starts showing progress immediately after submit.
• no critical console errors across Chrome mobile simulation.
• no duplicate records after five repeated test submissions.

Prevention

Once fixed I would put guardrails around speed so it does not regress next sprint.

1. Set a performance budget:

• JavaScript bundle growth capped at +10 percent per release.
• LCP regression threshold set at +200 ms maximum before review.

2. Add CI checks:

• Lighthouse CI on main routes.
• build fails if core metrics drop below target thresholds.

3. Review third-party scripts monthly:

• remove unused pixels,

widgets, trackers, embeds, anything that does not directly support conversion

4. Keep API security tight: - least privilege access for Airtable tokens, Make credentials, and Cloudflare settings

5. Log enough to debug but not enough to leak data: - redact prompts, email addresses, access tokens, and session secrets

6. Design for perceived speed: - show skeletons, optimistic UI, clear progress states, and short copy that tells users what happens next

7 . Test real devices regularly : - low-end Android , older iPhones , slow Wi-Fi , and Safari mobile

If you skip these guardrails , performance will drift back within weeks .

When to Use Launch Ready

Launch Ready is the right sprint when you already have a working product but deployment hygiene is hurting launch speed , trust , or conversion .

- domain setup , email setup , Cloudflare , SSL , deployment , secrets , monitoring , DNS , redirects , subdomains , caching , DDoS protection , SPF / DKIM / DMARC , production deployment , environment variables , secrets handling , uptime monitoring , and a handover checklist .

Use it when : - your chatbot works in staging but production feels fragile , you need faster pages before paid traffic starts , you suspect broken redirects , bad SSL , or weak caching , or you want me to harden the launch surface before users see it .

What you should prepare : - domain registrar access , Cloudflare access if already set up , hosting access , Make.com account access , Airtable base access , email sending provider access , current environment variables list , and any known failure examples from users .

If your issue is mostly speed plus weak Core Web Vitals , Launch Ready fits well as the foundation sprint .

References

- https://roadmap.sh/frontend-performance-best-practices

- https://roadmap.sh/api-security-best-practices

- https://roadmap.sh/qa

- https://developers.cloudflare.com/

- https://support.airtable.com/

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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.*