Automation
AI Content Operations Platform
A workflow for producing, adapting, reviewing and publishing multi-format content with mandatory human involvement.
Context
Teams manually produced drafts, variants and channel adaptations of content across different formats.
Problem
Throughput grew linearly with team size, and tone and quality diverged across people and channels.
Constraints
Quality and tone control are mandatory; publishing only after human review.
Architecture
A pipeline: variant generation → channel adaptation → review → human involvement → publishing. Each stage has explicit state.
AI layer
A cheap model for drafts and variants, a strong one for final message adaptation where tone and accuracy matter.
Event model
A content request starts the pipeline; stages are events with preserved state, so the process can be paused and resumed.
Integrations
Channel publishers, an asset store and tone guidelines connected through a normalized layer.
Automation flows
Generating N variants, adapting to format, deduplication, publishing control by schedule and by event.
Infrastructure
Stage queues, publish idempotency, per-step token budgets.
Observability
Tracing of stages and cost, the share of assets that passed human review on the first try.
Results
Throughput scales without proportional team growth; tone became more consistent thanks to a single review layer.
Lessons
Human involvement is not a bottleneck but a quality loop; content operations is a stateful pipeline, not batch generation.