Editorial intelligence: stop drowning your team in manual research
Four hours per article on research, drafting, translation and review. Sources you cannot trace when challenged. A scale ceiling drawn by your headcount. There is a different operating model — a governed editorial pipeline that scales without growing the team.
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Your editorial pipeline is the ceiling
Hours per article, every article
Four-plus hours per piece on research, drafting, translation and review, and that cost compounds across the team and across every brand you cover. Hiring three more writers buys you a few more articles a week before the same wall returns. Headcount is not the lever your operation actually needs.
No audit trail when a fact is challenged
When a regulator, lawyer or reader challenges a claim, no one can show where the fact came from, who approved its inclusion, or which version of the source was consulted. The reputational and regulatory risk is already on the table, and the AI Act's transparency rules will only sharpen it. Auditable systems are not optional once AI is in the workflow.
Quality drifts as you grow
Different writers produce different quality. Adding a brand or a topic doubles the manual setup work. Bilingual output dilutes terminology one batch at a time. Without an encoded editorial standard and a governed pipeline, the only knob you can turn is hiring.
From manual drudgery to a governed pipeline
Audit your current pipeline
I sit with your editorial leads and walk every step from initial assignment to publication: research, drafting, fact-checking, translation, review, layout, scheduling. We measure throughput in real hours per article and identify where the time goes. Not in a slide deck: in a measurable workflow map.
Build the entity-resolved knowledge base
Your facts deserve a real home. I model the entities your articles depend on (people, organisations, products, topics, events), then build the ingest pipeline that turns sources into a queryable knowledge graph. Every fact is source-tracked, versioned, and ready for retrieval. The same architecture powers data research systems.
Wire AI agents with human gates
I design the multi-agent pipeline (research, draft, fact-check, voice consistency, translation), with human review gates where editorial judgement matters most. Every agent action is logged. Every revision is auditable. Models are routed by task: lighter models for volume, frontier models for judgement.
Measure and refine
I ship to your team and instrument the pipeline end-to-end: throughput, cost per article, quality scores, escalation rates. We tighten quality gates where the data shows drift and loosen them where the pipeline is over-engineering. The dashboard becomes the operating console for your editorial operation.
What this looks like in production
Research, drafting, fact-checking, translation, review: what used to take 4 hours per article now runs in 45 minutes through a governed pipeline. Lighter models handle volume tasks; Claude Opus and GPT-5.1 handle editorial judgement where it counts. Read how it was built.
People, organisations, events, products, topics: all entity-resolved, source-tracked and versioned. Fact-checking runs as RAG retrieval against the graph via Upstash Vector and text-embedding-3-small, not against a generic web search. Every claim in every article is traceable.
Unit, integration and end-to-end tests across the pipeline, held to a release standard rather than a demo standard. Langfuse traces every run with cost, latency and token usage. Your editorial operation runs on observable, testable software, not a folder of prompts.
5 agents, 8 steps, 3 human gates
- 5 specialised AI agents in an 8-step pipeline with 3 human review gates: agents handle volume, humans hold the editorial line
- Database-first architecture: every fact source-tracked and versioned, every revision auditable, no claim ships without provenance
- 9 brand profiles published in 2 languages, scaling without growing the team; the same pipeline serves more brands by configuration, not hiring
Three services, one pipeline
Content operations
End-to-end editorial pipeline that scales with your team, not against it: research, drafting, voice enforcement and translation in one governed flow.
AI workflow automation
The multi-agent orchestration that makes the pipeline reliable: control plane, audit trails, per-agent budgets, and quality gates at every stage.
Data research systems
The entity-resolved knowledge base that powers every article: sources, entities and provenance modelled as a queryable graph, not a folder.
Built for editorial rigour
Common questions
How long does it take to deploy an editorial intelligence platform?
Most engagements run three to five months from audit to a production-ready pipeline serving real articles. The audit and pipeline-mapping phase is two to three weeks; the knowledge base and entity-resolution layer takes four to six weeks; the multi-agent pipeline with human gates and quality checks takes another six to eight weeks; deployment and instrumentation overlap with the final build. You see working components weekly, not a six-month black box. The same engagement model applies to the underlying content operations build and the broader work of getting AI to actually work in production.
Can it work alongside our existing CMS, DAM and publishing stack?
Yes. The pipeline is designed as a layer on top of your existing stack, not a replacement. Articles produced by the pipeline are pushed to your CMS via API. Source documents are ingested from your DAM, drive or asset library. Translation memories, glossaries and editorial guidelines load directly into the agent context. Integration is part of the audit phase, not an afterthought: the goal is to remove drudgery from your existing operation, not to force a tooling migration. The same integration approach powers custom business tools built on top of legacy systems.
What about EU AI Act compliance and audit requirements?
Compliance falls out of building this correctly. The patterns the AI Act mandates (Article 14 human oversight dashboards, Article 12 audit trails, Article 9 risk management documentation) are precisely the patterns that make editorial pipelines reliable in production. Every agent action is logged with full provenance: which model, which sources, which prompts, which output, which approver. Human gates are wired into the pipeline by design. For a deeper look at the regulatory side, see EU AI Act compliance.
Will my writers still have agency over the final output?
Yes, explicitly. The pipeline is built around human review gates at the points where editorial judgement matters: angle selection, source weighting, voice calls, sensitive claims. Agents handle research, drafting, fact-checking and terminology preservation; writers handle judgement and final approval. Nothing publishes without a human signing off on the parts that require human signoff. The goal is to give your team back the hours that AI can absorb, so they can spend them on the parts that only they can do.
What's the cost compared to hiring more writers?
Frame the maths the way a CFO does. A senior writer in France costs roughly EUR 60–90k all-in per year and ships a fixed volume of articles. You pay that line indefinitely, and it climbs at every salary review. An editorial intelligence platform, by contrast, builds once over three to five months, runs for under three dollars per article in inference cost, and absorbs growth without growing the team. Most editorial operations cross into ROI inside twelve to eighteen months, sooner if they were going to hire anyway. The same logic applies to custom business tools replacing per-seat SaaS.
Stop trading hours for articles. Start scaling editorial without scaling headcount.
Bring your current editorial pipeline. We'll walk every step, measure in real hours where the time actually goes, and find the highest-leverage automation in your operation.
30-min call. No commitment. Reply within 24h.