Pillar

The autonomous content engine that researches, writes, illustrates, and edits an entire month of bilingual SEO articles — at the standard of a senior editorial team, without one. Built for brands that need to publish at scale and still sound human.

Pillar monthly-run dashboard — agent pipeline, latest deliverables, and quality stats

A full content team’s output. One autonomous run.

Agentiq built Pillar for a leading Indonesian aquaculture & fish-feed producer that needed a steady stream of high-authority articles across six product categories — in both Bahasa Indonesia and English — without growing an in-house content desk.

Pillar runs on seven specialised AI agents, each with one job, a curated toolset, and an explicit quality bar. They hand work off through a shared knowledge base and versioned files, so every claim is sourced, every angle is fresh, and a human can inspect any stage. The result is publish-ready content — not a first draft that still needs a writer.

“Pillar” is a portfolio showcase. Client identity and proprietary data have been anonymised under NDA; sample figures are representative.

Product highlights

  • It never competes with itself

    Every new concept is hashed and checked against a growing archive of past articles, so the same keyword cluster never cannibalises its own rankings.

    Angle-overlap check against archived article angles
  • It doesn’t read like AI

    A 12-rule anti-“AI tell” ruleset governs rhythm, transitions, and structure — then an editor agent scans every draft and fixes what slips through.

    Anti-AI-tell scan passing every check
  • It cites its sources

    No claim ships unverified. A research agent grounds every statistic against at least two authoritative bodies before a writer is allowed to use it.

    Claim verification with at least two authoritative sources

Feature breakdown

  • 01

    Seven specialists, one pipeline

    Instead of one model doing everything passably, Pillar splits the job across seven focused agents. Each is tuned to the cheapest model that can do its task well — judgment-heavy roles on the strongest tier, structured-fact roles a step down, the mechanical librarian on the lightest.

    Pillar's seven-specialist agent pipeline
  • 02

    Bilingual by design, not by translation

    The Indonesian article is written first as the canonical version. The English one is adapted — units converted, examples re-localised, idioms rewritten for an international trade audience — never a word-for-word translation. Both stay in lockstep on the facts that matter.

    Mirrored Indonesian and English versions of the same article
  • 03

    An editor that signs off — or sends it back

    The last agent in the writing chain is an editorial gate. It runs an anti-AI scan, a fact spot-check, and a full on-page SEO checklist, fixes what it can in place, and issues a binding verdict. Only work it marks approve gets filed; anything weaker is held back for a human — automatically.

    Editor QC report with scores and a binding approve verdict
  • 04

    Original imagery, generated in-house

    Every article ships with three original visuals — a hero, an explainer, and an editorial photo — generated on a fast image model and shared across both language versions. No stock-photo licences, no design queue, roughly nine cents an article.

    AI-generated infographic showing a feed-calculation flow: biomass times feeding rate converting to bagged feed

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