We built and run ClawdBot — an end-to-end automation that turns government PDFs and news notifications into publish-ready articles with custom imagery in about ninety seconds. Now we build variants of the same stack for publishers, newsletters, and content-heavy businesses.
Most "AI content automation" in the wild is a ChatGPT wrapper with a publish button. ClawdBot is what happens when you let a team that runs a 10M-visitors-per-month site design the pipeline for their own production use. Five major versions, running today, producing articles that actually rank.
PDFs, notification URLs, RSS feeds, email inboxes. We OCR scanned docs, parse layout-heavy PDFs, and extract structured fields (dates, eligibility, fees, quantities) with a combination of rules and LLMs.
We run the source through multiple LLMs in parallel (Gemini, Claude, Qwen via Ollama, Grok) and compare outputs. Discrepancies trigger human review; agreement publishes with confidence. Single-model pipelines are fragile — multi-model is not.
Template-driven article composition with variable sections (overview, eligibility, timeline, how-to-apply, FAQ). Every section traceable back to source. No hallucinated fields — if the source doesn't have it, the article doesn't claim it.
ImageMagick-based composition with brand templates, WebP conversion, and edge delivery via Cloudflare R2. Every article gets a featured image, OG card, and thumbnail without touching Canva.
A review UI where an editor sees the proposed article side-by-side with the source, can edit inline, and approves in one click. Autopublish available for high-confidence categories; mandatory review for the rest.
Direct publish to WordPress, Ghost, custom PHP CMSes, or flat-file generators. Full control over slug, categories, tags, schema, and internal linking — because we've watched SEO die when automation gets sloppy here.
The ClawdBot architecture transfers well to any content-heavy workflow where structured-ish source material needs to become publishable content at high frequency, without hallucinations, and with predictable quality. Common mappings:
AI work doesn't fit in a one-size-fits-all box. We offer three engagement shapes depending on whether you want us to build, to host, or both.
A full ClawdBot-style pipeline built for your specific source material, output format, and CMS. Delivered as code you own, deployable on your infrastructure, with documentation and training.
We build the pipeline and run it on our infrastructure. You send source material (or we pull it from your feed) and receive publish-ready output. LLM costs billed at pass-through.
You already have AI pipelines running. They're slow, expensive, flaky, or hallucinating. We audit what you've built, identify the actual bottlenecks, and deliver a fix roadmap. One-time engagement.
Opinionated because we've been wrong before, and learned.
Every claim in every output article is traceable to a source span. If the LLM can't cite where it got a number from, that number doesn't ship. This is the single biggest difference between pipelines that last and pipelines that get banned.
Anchoring a pipeline to a single model is taking on enormous vendor and quality risk. We design for model substitution from day one — Claude, Gemini, Qwen, GPT, local inference — all swappable without rewrites.
"Fully autonomous AI content" is a fantasy that breaks brands. We build for editorial workflow — fast review, easy correction, clear confidence scoring — because that's what actually ships quality content at scale.
For high-volume pipelines, running quantized open models on your own hardware cuts costs by 10–100x and kills latency spikes. We architect hybrid pipelines where local models do the bulk and hosted models do the hard parts.
AI-generated content at scale dies fast without deliberate structure — internal linking, schema, entity consistency, original perspective. We build the SEO rigor into the pipeline, not bolted on afterward.
Every call logged. Every cost attributed. Every failure retriable. Most AI pipelines we audit can't even tell you which model produced yesterday's worst article. That's the difference between a prototype and production.
The ones that come up on nearly every scoping call.
Tell us about the content operation you want to automate. We'll come back with a rough architecture, a realistic cost model, and a build quote.