Cases & Playbooks

Selected systems, evidence and playbooks

A practical view of the kinds of growth loops and agent workflows Fabrick Media designs, with context, constraints and reusable learnings.

Selected experience includestrivagoCoIQinneOviva

Workflowsystemized

AI Agenttask complete

Pilotsignal reviewed

Learningreadout

Featured Cases

Marketing Operations

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: Less manual assembly and a clearer weekly decision rhythm.
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.

D2C Growth

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: More usable creative variants and clearer guidance for the next production cycle.
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.

Data & Intelligence

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: Faster report preparation and more consistent decision context.
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.

Baseline the workflow, map the system, instrument the pilot, measure the result and hand over the operating rhythm.

Document the current workflow, inputs, owners and constraints.

Translate the workflow into a repeatable system with clear handoffs.

Add tracking, QA checks, readouts and approval points.

Review the pilot against the practical success metric.

Document the operating rhythm and next iteration.

Start with the bottleneck, then turn it into a weekly system for experiments, readouts and reusable learning.