Content pipelines your editorial team will love
From research to publication in any language. Brand-aware AI that knows your style guide, your editorial standards, your voice.
Your content process doesn't scale
Days from brief to publication
Your editorial process involves manual research, multiple drafts, email back-and-forth, review meetings, and finally a copy-paste into the publishing tool. What takes days could take minutes if the research feeding the content were structured and the pipeline were automated.
One format per communication
Your team produces a press release. That's it. The same story could be a LinkedIn post, a blog article, an X thread, and a press kit — but adapting content to each channel is manual work nobody has time for. You're leaving reach on the table.
Brand voice drifts across channels
Without encoded editorial standards, voice consistency depends on who writes what and when. Add translation into the mix, and your brand sounds like a different company in each language. The same governance patterns that make AI workflows reliable apply to content operations.
From brief to publication
Editorial audit
I map your content workflow — from initial brief to final publication. We identify where time is lost, where quality drops, and where automation will make the biggest difference. The audit covers channels, languages, review cycles, and brand consistency enforcement.
Pipeline design
I design the pipeline stages, agent roles, and quality gates. Each stage has a clear input, output, and success criteria. Voice enforcement and editorial standards are encoded into the system — not left as guidelines people forget to follow.
Build
Production development with working pipeline stages delivered weekly. You see real content flowing through quality gates — research being structured, drafts being generated, editorial reviews catching issues, translations preserving terminology.
Deploy & iterate
Ship to your team, measure output quality, expand to new channels and languages. The pipeline adapts to your evolving editorial needs — new content types, new channels, and new languages plug into the existing governance framework.
Two pipelines, one capability
Research, write, caption, review, voice consistency, voice review, and translation — a production editorial pipeline where every article is orchestrated by specialised agents and backed by sourced data. A governed editorial operation — every article reviewed, fact-checked, and voice-verified before publication.
Press release, LinkedIn post, X thread, blog article, and press kit — generated in parallel from a single enriched brief. Designed and scoped for a corporate communications team where a single communication event warrants five channel-optimised outputs.
What takes days of manual work — research, writing, review, translation — runs in minutes through governed automation. Bilingual output where a dedicated Translator agent preserves domain-specific terminology across languages. Read how it was built.
7 steps, zero shortcuts
- 7-step editorial pipeline: research, write, caption, review, voice consistency, voice review, and bilingual translation with terminology preservation
- Dedicated Voice agent enforcing brand personality across every article — editorial decisions encoded, not documented and forgotten
- Every published fact traced back to its source URL, date, and author — full provenance from research through publication
Built for editorial rigor
Common questions
How does the AI maintain our brand voice?
Brand voice is not a prompt suggestion — it's encoded in the system. In the editorial pipeline, a dedicated Voice agent reviews every piece of content against your voice specifications, editorial decisions, and domain glossary. A separate Editorial Judge enforces quality standards. Nothing publishes without passing both gates. For corporate communications, brand foundation documents — editorial charter, visual charter, glossary, key messages — are loaded into the generation context.
Can this handle multiple languages?
Yes. The editorial pipeline includes a dedicated Translator agent that handles EN↔FR with terminology preservation — not generic machine translation, but domain-aware translation that respects your editorial decisions and nomenclature. The same architecture extends to any language pair. Building multilingual tools is a core capability.
How is this different from ChatGPT or other AI writing tools?
ChatGPT generates text from a prompt. A content operations pipeline generates content from structured, sourced data through a governed multi-stage process with quality gates, voice enforcement, editorial review, and translation. The output is traceable — every fact has a source, every editorial decision is logged, every translation preserves your terminology. It's the difference between a text generator and a production editorial system.
How do you quality-check AI-generated content?
Through deterministic quality gates at every pipeline stage. An Editorial Judge reviews against your editorial standards. A Voice agent enforces brand consistency. A Translator preserves domain terminology. Each gate can approve, reject with specific feedback, or escalate to a human reviewer. The pipeline doesn't skip steps — every article passes every gate before publication.
Can it integrate with our existing CMS?
Yes. The pipeline produces structured content that can be pushed to any CMS, publishing platform, or social media tool via API. Integration with your existing stack — whether it's a custom-built platform, a headless CMS, a mobile application, or a financial platform with reporting needs — is designed into the architecture from the start.
Your editorial team deserves better tools.
Let's map your editorial workflow and find where automation adds the most value.