Results
The challenge
A Hyderabad-based B2B SaaS company selling DevOps tooling to engineering teams had a clean hypothesis: deep technical content is their best demand-gen channel. The problem was production. Two content writers (one in-house, one freelance) were capped at ~4 posts a month — 1,500–2,500 word pieces requiring technical depth, accurate code examples, and domain credibility.
At that cadence, organic traffic was growing but slowly. Competitors publishing 20+ posts a month were pulling ahead on search rankings. Hiring more writers wasn't a fast fix — quality technical writers are scarce, expensive, and take months to ramp.
The founder asked: could AI augment content production without sacrificing the quality bar?
What we built
Arthat built a multi-stage content production pipeline designed for senior technical content:
Stage 1 — Ideation + briefing agent: Given a topic cluster (e.g., "CI/CD pipeline observability"), the agent produces 20–40 ranked blog post ideas, each with: target keyword, search volume estimate, competing content analysis, structural outline, suggested angle.
Stage 2 — Research agent: For each selected idea, a research agent pulls authoritative sources (documentation, Github issues, academic papers, relevant Stack Overflow threads), extracts specific facts and code examples, and produces a structured research document.
Stage 3 — Draft agent: Using the research document + the brand's voice exemplars, a drafting agent produces a full first draft — typically 1,500–3,000 words, with code samples, diagrams described in text, and proper internal links.
Stage 4 — Editor agent: A second LLM pass acts as a senior editor — checking factual claims, improving prose, enforcing brand voice, flagging weak sections. Annotated with edit reasoning so a human can review.
Stage 5 — Human review: The in-house content lead reviews the annotated draft — typically spending 20–40 minutes per post on factual verification and tone polish. Approves or sends back for revisions.
Stage 6 — Publishing agent: Approved posts are formatted, meta tags added, internal linked appropriately, scheduled in Ghost CMS. Post-publish, the agent monitors indexing, rankings, and AEO (citation in AI search).
Critical design decisions:
- Code examples are human-verified. The agent can generate code, but before publishing, a human runs it or reviews it for correctness. Zero tolerance for published broken code.
- Voice exemplars come from top-performing existing posts. The agent learned to sound like the company's best writing, not like a generic LLM.
- AEO structure is baked into every post. Clear Q&A sections, definition paragraphs, structured factual claims that LLMs can cite cleanly.
The content lead's role shifted from "producing" to "directing." Less writing, more editing, strategizing, and ensuring quality.
Arthat AI
Want results like these at your company?
We built this for them. We can build something similar — or something completely different — for you. Start with a 30-minute discovery call and we'll tell you honestly whether AI is the right tool for your situation.
