AI Content Engine Development

Reference architecture

1. Brief Input
2. Research Pipeline
3. LLM Drafting
4. Editor Review
5. Fact-Checking
6. CMS Publish
7. Performance Feedback

A typical AI content engine: a brief feeds the research pipeline, the drafting model produces a first pass, editors review and approve, fact-checking verifies claims and citations, content publishes to your CMS, and performance data flows back to improve the next brief.

Frequently asked questions

• How do you handle brand voice consistency across hundreds of pieces?+

  We ingest your style guide, build few-shot prompts around it, and back the result with a voice evaluation harness. Every piece runs against the same voice criteria, so quality holds whether you ship ten articles a month or a thousand.

• Can you fact-check AI output at scale?+

  Yes. We build citation verification and structured-source retrieval into the drafting flow, so claims are grounded in real sources rather than model memory. Hallucination detection patterns flag unsupported statements before content reaches an editor, and the system surfaces the citations behind each claim for review.

• Do you integrate with our existing CMS?+

  Almost certainly. We ship production sync with Webflow, WordPress, Sanity, and Contentful, and we integrate across LLM providers, SEO data sources, translation APIs, and distribution tools. If a platform has an API, we can wire it in. If it doesn’t, we’ll tell you the honest workaround.

• What about multi-language content?+

  We handle translation orchestration with locale-specific tone adjustment, not just literal translation. Content is drafted, translated, and re-checked for voice and accuracy in each target locale, with tools like DeepL and Google Translate API wired into the pipeline where they fit your quality bar.

• How do you architect for cost at scale?+

  Cost is a first-class design constraint, not an afterthought. We use caching to avoid re-generating repeated work, model routing to send each task to the cheapest model that can do it well, and prompt compression to cut token spend. The result is predictable per-piece economics as your volume grows.