Editorial intelligence platform: 4 hours to 45 minutes
An AI-governed editorial platform with 7 specialised agents, a 7-step pipeline with quality gates, and institutional-grade output — for under $3 per run.
30-min call. No commitment. Reply within 24h.
Content production doesn't scale
Manual production bleeds time
Hours of raw audio become weeks of editorial work. Research, transcription, writing, fact-checking, translation — each step requires a specialist, and each specialist costs. A single magazine issue ties up an entire team. Automating content operations isn't optional when your production cadence outpaces your headcount.
AI output fails editorial standards
Off-the-shelf AI writes generic content that fails institutional requirements. No source verification, no editorial voice compliance, no audit trail. Under EU AI Act transparency rules, AI-generated content must be traceable — who wrote what, from which sources, with what confidence level.
Scaling means losing governance
One AI pipeline is manageable. Seven agents across two departments, each with different models, budgets, and quality requirements? That demands a structured governance layer — not more prompts.
An AI newsroom
Audio processing pipeline
Raw audio ingested via smart-split at silence boundaries, normalised to broadcast standards with 2-pass loudnorm, then transcribed via Whisper with speaker identification. Voice synthesis uses Qwen3-TTS for review playback. GPU orchestration runs on Vast.ai through Docker, scaling burst capacity without standing infrastructure cost. SHA256 content-addressed caching means reprocessing identical audio costs nothing — every transcription is replayable from cache.
Agent architecture
7 specialised agents across 2 departments: Data Operations and Editorial. Each agent has a defined role, model assignment — Sonnet for volume, Opus for judgment, GPT-5.1 where the routing rule prefers it — budget ceiling, and governance rules coordinated through Paperclip, a central control plane that owns budgets, approvals, and audit trails. Agent orchestration runs on Mastra.ai with the MCP SDK exposing 21 typed tools to the database layer.
7-step editorial pipeline
Research, Write, Caption, Review, Voice Consistency, Voice Review, Translate. Every stage has quality gates: RAG-based fact-checking against source transcriptions via Upstash Vector and text-embedding-3-small, editorial DNA compliance scoring, and confidence thresholds that tighten with each iteration. A confidence decay system stops the pipeline before it grinds into infinite revision loops on a stubborn section. The buyer-side playbook for this approach is documented under editorial intelligence.
Non-technical interfaces
A dashboard for entity management and pipeline monitoring, plus a content studio designed for users who have never touched an AI tool — brief-assisted workflows, parallel generation across 5 formats, and a collaborative editor with review workflows.
Production-grade, not prototype-grade
Organised into 2 departments — Data Operations and Editorial — each with distinct model routing. Lightweight models handle transcription refinement; frontier models handle editorial judgment. No single-model bottleneck.
A 12-principle calibration system verifies every generated section against institutional voice specifications. Measured against published reference issues — not estimated. Each principle is weighted by priority, with confidence decay preventing infinite revision loops. Data provenance built into every claim.
Smart model routing keeps costs predictable: per-section budget caps at 15%, a 70% warning threshold, and a 2x circuit breaker that halts runaway spending. Crash recovery resumes from the last checkpoint — no wasted tokens. Every run is observable end-to-end through Langfuse, with 1,500+ tests across unit, integration, and end-to-end coverage holding the pipeline to a release standard, not a demo standard.
Capabilities behind this project
AI workflow automation with multi-agent pipelines, human oversight and audit trails. Production-grade systems, not chatbot demos. Built by a senior engineer.
Editorial workflow automation, from research to publication. Brand-aware AI pipelines, quality gates, multilingual output. Built by a senior engineer.
Automated data research systems with coverage scoring, gap detection and source-level traceability. Manual collection becomes systematic intelligence.
Built for production
Common questions
What does a project like this cost and how long does it take?
A multi-agent editorial pipeline at this scope (7 agents, 7-step pipeline, RAG fact-checking, 1,500+ tests) typically takes 4-5 months kickoff to production and lands in the 100-150k EUR range. The build phases naturally — audio ingestion, then the editorial pipeline, then the dashboard — so you see working stages every week, not a single big-bang release. Engagements are direct: I write the code myself, no agency layer, and you own the codebase outright. A leaner scope (single department, 3 agents, no audio) fits in the 50-80k EUR / 3-4 month band. The AI workflow automation service page walks through the engineering approach in more depth.
What happens when an agent produces factually incorrect content?
Every factual claim is verified through RAG-based fact-checking against source transcriptions, event programs, and speaker bios. The fact-checker assigns confidence scores (0–1) to each claim. Below threshold, the section gets flagged for rewrite with the specific failing claims highlighted. The data research system ensures provenance is built into the data model — every fact traces to its source. That source attribution and audit trail is exactly what EU AI Act compliance requires when high-risk systems take effect.
Can this approach work for content that isn't audio-based?
The agent architecture is source-agnostic — audio processing is just the first ingestion module. The editorial pipeline, quality gates, and governance layer work identically with documents, web research, structured data, or any combination. Content operations built this way adapt to new input types. The same approach powers custom business tools like the multi-module business platform.
Need a similar pipeline?
Let's talk about automating your editorial or research workflow.
30-min call. No commitment. Reply within 24h.