AI Publishing & Content Operations Platform
A publishing operations platform for a leading Australian financial media organisation, coordinating research, AI-assisted drafting, quality controls, human approvals, media assets and downstream CMS publishing.
AI assists, editors approve.
- 1. Research and source material
- 2. AI-assisted editorial workflow
- 3. Human review and approval
- 4. Published content
- Role
- Platform architecture and delivery leadership
- Serves
- Editors, content operators and publishing stakeholders.
- Core technologies
- React · TypeScript · Firebase · Cloud Functions
Executive overview
The problem. High-volume financial publishing involves many moving parts, research, drafting, checking, imagery and CMS publishing. Speeding this up with AI risked weakening editorial judgement and accountability.
Who it serves. Editors, content operators and publishing stakeholders.
What Daynis did. Platform architecture and delivery leadership. A publishing operations platform for a leading Australian financial media organisation, coordinating research, AI-assisted drafting, quality controls, human approvals, media assets and downstream CMS publishing.
Why it matters. Accelerates complex publishing workflows while keeping editorial judgement, quality control and accountability firmly in the loop.
Business architecture and impact
Capabilities created
- Multi-stage editorial workflow from research to publication
- AI-assisted drafting with human approval gates
- Media library with duplicate detection
- Scheduled and on-demand processing
- Synchronisation with the downstream CMS
Accelerates complex publishing workflows while keeping editorial judgement, quality control and accountability firmly in the loop.
System architecture
- 01
Editors & operators
Where requests and source data originate.
- 02
React + TypeScript workspace
The interface people use day to day.
- 03
Multi-stage workflow (Cloud Functions) + Scheduled & on-demand jobs
Orchestrates requests and enforces business rules.
- 04
AI drafting & research services
Models applied under defined, reviewable constraints.
- 05
Firebase content store
The governed system of record.
Key flows
- Editors & operators to React + TypeScript workspace
- React + TypeScript workspace to Multi-stage workflow (Cloud Functions)
- Multi-stage workflow (Cloud Functions) to AI drafting & research services
- Scheduled & on-demand jobs to Model-processing jobs
- AI drafting & research services to Deterministic validation
Technical depth
Highlights
- React and TypeScript application on Firebase and Cloud Functions
- Orchestration across multiple AI and research services
- Specialised model-processing jobs
- Deterministic content validation before approval
- Role-based access and operational controls
Architecture decisions
Humans approve, AI assists
Approval gates are part of the workflow model, not an afterthought: nothing publishes without sign-off.
Validate deterministically
Rule-based content checks run before a human reviews, so reviewers spend time on judgement rather than mechanics.
Multiple services, one workflow
Research and AI providers are orchestrated behind a single workflow so they can change without disrupting editors.
Security and governance
- Role-based access to workflow stages
- Operational controls and approval accountability
- Client identity and branded material kept confidential
What I led
- Platform architecture and technical direction
- Stakeholder engagement with editorial and business teams
- Delivery governance for AI features in a quality-critical setting
Evidence and links
Evidence: Confidential case studyShared at a public-safe level.
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