Content Automation
Platform
An end-to-end AI-powered content production system
built on Airtable for a digital marketing agency
BEATRIZ BRAGA
Solo Builder
Airtable
Make
OpenAI / OpenRouter
Tally Forms
Bannerbear
01 — CONTEXT
The Business Problem
Marketing Agency / Content creation couldn’t scale with client growth
01 Manual & Repetitive Briefing
Manual onboarding for every client
No centralized source of truth
Time wasted re-entering the same information
02. Inconsistent Brand Voice
No standardized briefing system
Content varied across creators
Brand consistency was compromised
03 No Path to Scale
Growth required hiring more people
No operational leverage
Model was not scalable
→ Content production depended on people, not systems.
02 — THE SOLUTION
A centralized system to automate content creation
while preserving brand consistency
INPUT
Client Brief
Tally Form
Brand Voice
PROCESS
Airtable as the
heart of the system
Make.com
AI Generation
OUTPUT
Content Creation
Design Assets
Content recycling
→
→
From manual, inconsistent workflows → to a scalable, system-driven content engine
02 — THE SOLUTION
Built End-to-End
Elements created by me in collaboration with the operational team and leadership.
D
Data Architecture
Relational tables with linked records, lookup chains, and formula fields > the operational backbone of the system.
A
Airtable Build
All tables, views, field logic, and base configuration built from scratch, including formulas and automation triggers.
S
Automation Scripts
JavaScript scripts embedded in Airtable automations for webhook triggering and data extraction.
M
Make.com Scenarios
Scenarios orchestrating AI content generation and writing structured outputs back to Airtable dynamically.
I
Integrations
Tally, OpenAI via OpenRouter, and Bannerbear, external tools fully connected to the automated content pipeline.
Architecture Overview
TALLY
FORMS
Client intake
& profile quiz
AIRTABLE BASE
Clients
Profiles
Design Set
Planning
Prompt Library
Social Posts
Designs
Content Creator
MAKE.COM
Automation router
OPENAI
OPENROUTER
LLM content generation
BANNERBEAR
Design automation
Airtable
Make.com
AI / LLM
Design Automation
03 — CLIENT JOURNEY · STEP 1
Client Onboarding
BEFORE
2–3 onboarding calls required per client
Information scattered across notes and documents
No standardized briefing process
Rework needed for every new content request
↓
AFTER
Automated onboarding
Centralized client data in Airtable
Standardized brand profiles for every client
Reusable context across all future content
HOW IT WORKS
1
Client completes a structured intake form (Tally)
2
Responses are scored and mapped to a brand profile
3
Data is processed and stored in Airtable
4
Client gets a persistent profile, history, and design system
↓ Onboarding time
from 3 meetings → 1 automated step
Airtable · Client Profile Record
03 — CLIENT JOURNEY · STEP 1
Structured brand data captured at onboarding, reused across every content generation request
EW
Dr. Emily Walsh
Aesthetic Medicine
PROFESSIONAL
Language
English (EN)
Platforms
Instagram · LinkedIn
Active since
January 2024
VOICE TONE
Authoritative · Data-backed · Results-oriented
FORMALITY LEVEL
High — Professional register
MAIN TOPICS
Skin Health · Anti-aging · Aesthetics · Wellness
TARGET AUDIENCE
Women 35–55 · Urban · Upper-middle class
EDITORIAL STYLE
Educational · Evidence-based · Long-form friendly
CALL-TO-ACTION
"Schedule your consultation"
TO AVOID
Slang · Competitor mentions · Aggressive promotions
CUSTOMER AVATAR
Career-driven · Invests in health · Researches before deciding
04 — CLIENT JOURNEY · STEP 2 & 3
Content Planning & Creation
From scattered, manual content creation → to an AI-driven, structured content pipeline
BEFORE
Content created without access to client history
No standard process — each writer worked differently
Hours lost searching past meeting notes and briefs
Scattered across multiple platforms and tools
↓
AFTER
AI generates monthly plan from client's brand profile
Plan reviewed and approved before production starts
All posts created automatically, caption, date, visual direction
Team reviews and requests edits
HOW IT WORKS
1
Team selects client — brand data auto-fills from Airtable
2
One click triggers AI to generate full monthly content plan
3
Client reviews and approves the plan before production
4
Factory runs: all posts created automatically with captions + dates
↓ Content production time
from 5 hours per client → 1 hour
Airtable · Content Planning Pipeline
04 — CLIENT JOURNEY · OUTPUT EXAMPLE
Content Pipeline
Stage 1 generates the monthly plan — Stage 2 produces every individual post from it
STAGE 1
Monthly Planning
Client
Dr. Emily Walsh
Month
March 2026
Format
Instagram · Reels · Carousel
Total Posts
12
AI Status
✓ Done — Awaiting approval
CONTENT THEMES
Anti-aging skincare routines
Post-procedure recovery tips
Education: what is aesthetic medicine?
STAGE 2
Social Post Production
POST 03 · MARCH 7, 2026
"Skin doesn't lie. After just one microneedling session, the results speak for themselves — here's what the first 48h look like."
Visual direction:
Elegant close-up of skin texture · Cool tones · Minimal overlay text
Approved
POST 07 · MARCH 19, 2026
"The most common question I get: 'How early is too early to start aesthetic treatments?' The answer might surprise you."
Visual direction:
Professional headshot style · Warm lighting · Branded frame
Waiting Approval
Planning Table
Social Media Table
Prompt Library
Designs
05 — CLIENT JOURNEY · STEP 4
Content Repurposing
From one approved post → to content adapted for every channel and format
BEFORE
Newsletter, video script.. — each written from scratch
Same core idea reformatted 4× with no consistency across channels
Hours spent manually adapting content for each platform
Brand voice and tone drifted with every new format
↓
AFTER
One approved post triggers repurposing automation in Make
AI adapts the message to newsletter, video script, and carousel
HOW IT WORKS
1
Approved social post triggers the repurposing automation
2
AI identifies the core idea, angle, and brand voice of the post
3
Format-specific versions generated: newsletter, video, carousel
4
All repurposed outputs land in Airtable, linked to source post
×4 Content output per approved post
Airtable · Content Repurposing Interface
Content Repurposing Creator
Prompt Library
Automation Scripts
Script 1 — Planning Trigger
TRIGGER
Record created in Planning — Content Type = "Planning"
ACTION
Sends RecordID to Make → LLM generates the full monthly content plan
Stable
Script 2 — Social Post Trigger
TRIGGER
Record created in Planning — Content Type = "Social Media"
ACTION
Sends RecordID + TableID + BaseID + OutputTableID → LLM generates a single post
Stable
↑ Same base trigger — filter distinguishes Planning vs. Social Media type
Script 3 — Output Normalizer
TRIGGER
When LLM output arrives from Make and is written to the Social Posts table
ACTION
Regex parses the raw LLM output and writes each piece of data to the correct Airtable field — maintaining data integrity across all content types
Unified
Replaces 2 separate extractors — one consistent logic for all content types
FIELDS EXTRACTED BY REGEX
Post Title
Name / identifier of the content piece
Publication Date
Scheduled date parsed from LLM output
Caption
Full caption text — multiline, preserved as-is
Client Name
Linked back to the correct client record
Airtable Automations and App Scripts
06 — INTEGRATIONS
The middleware that receives Airtable triggers, processes data, calls every tool in the stack, and writes structured results back
TRIGGER
Airtable
Record created or updated
TRIGGER TYPES
Planning record created
Social post record created
→
ORCHESTRATOR
Make.com
Automation platform — listens to Airtable, processes payloads, calls APIs, and writes structured results back
WHAT MAKE DOES IN EACH SCENARIO
↔
Routes data between Airtable and any connected app
⚙
Parses, transforms, and filters each incoming payload
Make.com · Automation Scenarios
TEAM KANBAN
Airtable Interfaces
CLIENT DASHBOARD
RESULTS · MEASURED IMPACT
Concrete outcomes from implementing the full Content Automation system
1 step
was 3 separate meetings
ONBOARDING
Fully automated | zero human intervention required to bring a new client on board
1 hour
was ~5 hours per client
CONTENT PRODUCTION
Monthly content time reduced
15 clients
was 10 with the same team
TEAM CAPACITY
50% more accounts managed with no added headcount or working hours
1 database
did not exist before
CONTENT LIBRARY
A structured, searchable content base built from scratch — every post saved and reusable
CHALLENGES AND RESOLUTIONS
CHALLENGE
HOW IT WAS SOLVED
1
Mapping an unstructured operation into a clear process
No documented steps, no defined inputs or outputs anywhere
→
Deep-mapped every workflow stage, defined inputs and outputs for each step, then rebuilt the full operation as a structured Airtable base
2
Defining data standards so AI outputs were actually usable
No schema, no field conventions — AI returned freeform text
→
Established field schemas per table and ran iterative prompt refinements until outputs consistently matched the expected structure
3
Separating planning from content production
Both stages were treated as one blurred, unmanaged process
→
Split into two independent tables with separate automations
4
Getting consistent, structured output from the AI
Raw AI responses arrived in inconsistent formats — unusable as-is
→
Built the Output Normalizer automation to parse, clean, and route each field to the correct Airtable column automatically
5
Keeping human approval without creating a bottleneck
Manual review was essential but disrupted the automated flow
→
Kanban interface with defined stages (Editing → Waiting Approval → Approved) — one human touchpoint, zero disruption to the pipeline
Thank You
+55 81 991688491