What kinds of AI systems has Fabrick Media built?

Fabrick Media has built AI growth marketing systems — including weekly experiment loops, creative briefing pipelines and landing-page testing cadences — and governed agent workflows for operational use cases such as CRM enrichment, reporting QA, Slack approval routing and content repurposing. Each engagement produces documented playbooks, templates and readouts the team can keep running independently.

What is an AI growth playbook?

An AI growth playbook is a documented operating guide that defines the owner, cadence, tools, prompts, templates and success measures for a specific growth or workflow system. Every Fabrick Media engagement produces a playbook so the team can run the system independently after the engagement ends.

Cases & Playbooks

Growth systems and AI workflows your team can actually run

See how Fabrick Media turns scattered marketing work into repeatable reporting, creative testing and agent-assisted operating systems.

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Representative client systems

Marketing Operations

Marketing Workflow Operating System

Context: Marketing team with recurring reporting, content preparation and campaign coordination.
Baseline: Context lived across documents, dashboards and messages, slowing weekly decisions.
System: Agent-assisted reporting and content-prep workflow with QA and human approval.
Implementation: Intake template, prompt library, review checklist, dashboard readout and operating cadence.
Result: Reduced weekly reporting assembly by replacing scattered inputs with one reviewed decision readout.
Learning: The workflow worked best when approval ownership was explicit.
Constraints: Not designed as a fully autonomous publishing system.
Period / source: Pilot pattern; client-specific details anonymized.

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D2C Growth

UGC Growth Loop for D2C

Context: D2C team needing more usable creative inputs for paid social testing.
Baseline: Creator inflow, briefs and performance feedback were not connected.
System: Creator pipeline, structured briefs, variant production and learning repository.
Implementation: Creator board, briefing templates, naming rules, readout template and weekly cadence.
Result: Increased the volume of test-ready creative by connecting creator sourcing, briefs and performance feedback.
Learning: UGC improves faster when creators receive clear examples and constraints.
Constraints: Performance depends on offer, media buying and market conditions.
Period / source: Representative growth-system pattern; details anonymized.

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Data & Intelligence

Agent-Based Reporting Workflow

Context: Team producing recurring reports from multiple data and knowledge sources.
Baseline: Manual collection and interpretation delayed decisions.
System: Agents collect inputs, draft summaries, flag anomalies and route output for review.
Implementation: Source list, data checks, anomaly prompts, summary format and human approval gate.
Result: Shortened report preparation by routing data collection, anomaly checks and summaries through a reviewed agent workflow.
Learning: QA prompts need clear thresholds and named review owners.
Constraints: Human review remains required for interpretation and final recommendations.
Period / source: Agent workflow pattern; details anonymized.

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Playbooks

Angle Machine

Goal: Prioritize stronger messages
Inputs: Research, reviews, ads data
Steps: Mine signals, cluster angles, score hypotheses, brief tests
Cadence: Weekly
KPI: Test velocity and winner rate
Output: Ranked angle backlog
Owner: Growth lead

Landing Page Testing

Goal: Improve conversion learning
Inputs: Traffic data, offers, page analytics
Steps: Map friction, draft variants, launch tests, document readouts
Cadence: Biweekly
KPI: Conversion signal and learning quality
Output: Page variant roadmap
Owner: Growth/Product

Creator Pipeline

Goal: Increase usable UGC supply
Inputs: Creator list, briefs, examples
Steps: Source, brief, produce, tag, reuse
Cadence: Weekly
KPI: Usable variants per cycle
Output: Creator production board
Owner: Creative lead

Agent Workflow Blueprint

Goal: Design a controlled workflow
Inputs: Process map, tools, risks
Steps: Map states, assign agents, add QA, define approvals
Cadence: Project
KPI: Cycle time and error visibility
Output: Workflow spec
Owner: Ops lead

Content Repurposing

Goal: Turn one source asset into channel-specific outputs
Inputs: Source content, audience, channels
Steps: Extract themes, adapt formats, QA, approve
Cadence: Per asset
KPI: Approved variants per asset
Output: Approved asset set
Owner: Content lead

AI Research System

Goal: Speed up market and customer research
Inputs: Sources, questions, constraints
Steps: Collect, compare, cite, summarize
Cadence: Per research sprint
KPI: Evidence quality and time saved
Output: Research brief and evidence log
Owner: Strategy lead

How cases turn into systems

We start with one recurring growth bottleneck, design the operating system around it, pilot it with human approval points, then hand over the cadence, prompts, checks and readout templates.

Baseline

Document the current workflow, inputs, owners and constraints.

Map

Translate the workflow into a repeatable system with clear handoffs.

Instrument

Add tracking, QA checks, readouts and approval points.

Measure

Review the pilot against the practical success metric.

Handover

Document the operating rhythm and next iteration.

Find the growth workflow worth systemizing first

Bring one reporting, creative, research or testing bottleneck. We will map the operating system and next pilot step.

Book a growth systems audit →