Editorial process

Why we publish this

The WallaWhats blog exists to help people get real value out of real-time social alerts — which accounts are worth tracking, when a WhatsApp alert beats an email digest, how to watch a competitor or a news wire without living inside the app, and so on. Those decisions shape how much noise you invite into your day. So we take the accuracy of what we publish seriously, and we think you should know exactly how the content on this blog is made.

What sets our content apart

• Product-verified. Every setup step, feature claim, and pricing figure is checked against the live WallaWhats platform at draft time. If a claim is untestable against production, it doesn't ship.
• Written by operators, not marketers. The person reviewing each post also runs the platform day to day, fields the support tickets, and reads the operational signals it emits.
• Comparisons are honest. When we compare WallaWhats to other ways of watching X — notification bots, saved searches, third-party dashboards — we cite their public docs and behaviour verbatim. Nothing is strawmanned. If they do something we don't, we say so.
• AI-assisted, human-owned. A named human approves every article before it publishes. AI helps with scale; human judgment gates the ship.

How each article gets made

1. Brief creation

Every article starts from a real signal — a support ticket pattern, a Search Console query cluster, or a customer question. The brief captures the audience, the promise the article makes, and the search intent it targets.

2. Research + product verification

We read the primary sources for any claim: the X (Twitter) API docs, the WhatsApp Cloud API docs, and any tool we're comparing against. Every setup flow is walked against the live WallaWhats platform. If something doesn't work as documented, we file it as a product bug before we ship the article.

3. Drafting

Most drafts are produced with AI assistance under a content-generation prompt that carries the article brief, the verified facts from step 2, and our voice guide. Drafts land in the repo as a .md file with full frontmatter, ready for review — no marketing template, no separate CMS.

4. Human review

A named reviewer reads every draft end-to-end. Their job is to catch:

• Any claim that isn't supported by the primary sources or doesn't reproduce against the live product
• Comparisons that overstate our position or misrepresent another tool's current offering
• Advice that would waste the reader's time or money if they took it — plan selection, alert-volume expectations, setup paths
• Copy that reads like marketing rather than practical guidance

5. Refinement + final read

The reviewer's flags come back as revisions, applied by AI against the original draft plus the reviewer's specific feedback. The reviewer then reads the revision to confirm every flag was addressed — not just acknowledged.

6. Localization

We translate every ship-ready article into 40 additional locales. Each translation preserves the accuracy of the original — screenshots and steps aren't rewritten, only prose is localized. The English article is always canonical; if a translated variant disagrees with English, English wins.

7. Publication + AI disclosure

Every published article carries a visible AI-content disclosure (bottom of every post) explaining that the article was AI-assisted and human-reviewed. This is a Google helpful-content signal and a promise to the reader that the provenance of what you're reading is not hidden.

Keeping content fresh

X and the tools around it move quickly. Interfaces change. APIs get deprecated. Best practices evolve. Content that was accurate when it shipped can become misleading a year later.

We run an automated SEO and freshness tracker over the whole corpus every week. It pulls Search Console signals, does a per-page on-page audit, and identifies posts that are ranking for outdated queries or referencing deprecated behaviour. The result becomes a work-list of concrete edits — mostly mechanical (fix an outdated figure, replace a deprecated step), some human-judged (whether an article needs a substantive rewrite vs. retirement).

Every post carries an Updated: date in the hero, distinct from its original publish date, so you know how fresh the content in front of you actually is.

Our editorial principles

Product accuracy over speed

We'd rather ship one accurate, verified article per week than four articles that skim a topic. If a claim can't be supported against the live product, it doesn't ship. Full stop.

Named humans review everything

Every article's editorial review is done by a specific, named person — see the byline of any post. We don't publish anonymously and we don't hide behind institutional voice.

Localization respects the source

Translations preserve the facts of the English original. Locale-specific prose, punctuation, and idiom are adjusted; the underlying claims are not.

Comparisons cite primary sources

Every claim about another tool links to that tool's own current docs or behaviour. If its offering has changed since we wrote about it, we correct the article — we don't leave stale comparisons in place.

Practical over clever

We optimize for whether the article helps a real person make a real decision — not for how clever the framing is or how novel the take.

Transparent about AI

Every AI-assisted article says so, at the bottom, in every locale, on every publication. No hedge, no marketing framing.

Our commitment

If you find something on this blog that's factually wrong, outdated, or misrepresenting another product, we want to know. Reach out via hello@wallawhats.com. We correct articles openly — every substantive edit bumps the Updated: date and preserves an audit trail in Git history.

Trust is the whole point of telling people what's worth their attention in the first place. We hold ourselves to the same standard.