Editorial policy
Every page on socialscalr.com is written by a real person on the SocialScalr team, fact-checked against the live product, and updated when the product changes. Here is exactly how that works.
Who writes our content
All SocialScalr help articles, glossary entries, and changelog posts are written by Mark Gabrielli, founder of SocialScalr and operator of an active LinkedIn-driven business. Engineering posts about the API and infrastructure are written by the engineer who built the feature.
Every article carries a visible author byline and a "last updated" date. Author bylines link to a real person with a verifiable LinkedIn profile.
How we source claims
- Product behaviour: verified directly against the running product. If we say SocialScalr does X, the article is updated whenever the X behaviour changes.
- LinkedIn behaviour: documented from LinkedIn's public help center, LinkedIn's terms of service, and direct observation across hundreds of customer accounts.
- Industry benchmarks: when we cite a number (e.g. "healthy acceptance rates are 25 to 45 percent"), it comes from our own aggregated customer data, not a third-party blog.
- Pricing and policies: mirrored from the live billing system. Discrepancies always resolve in the customer's favour.
Originality
We do not use generative AI to write our help articles or blog posts. AI tools are used internally for spell-checking, code review, and brainstorming, but every published sentence is written and reviewed by a human. We do not republish other companies' material.
Updates and corrections
When the product changes, the relevant help article is updated in the same release and the "last updated" date moves. If you spot something wrong, email [email protected] and we will correct it within one business day.
Corrections that are user-visible (price changes, removed features, security advisories) are also added to the changelog.
Sponsored content and conflicts of interest
SocialScalr does not run sponsored posts or paid placements. If we mention another product, it is because we use it ourselves or it is widely used by our customers; we disclose any commercial relationship inline.
Privacy of customer data
We never publish identifiable customer data. Aggregated benchmarks are computed over thousands of accounts and only at percentile resolution. Individual screenshots in documentation use sample / demo data, never real customer records.
AI and machine consumption
SocialScalr publishes a curated llms.txt and a fully inlined llms-full.txt so AI assistants can ground their answers about SocialScalr without scraping the whole site. JSON-LD schema is provided sitewide for the same purpose. We welcome inclusion in AI search results.
Contact
Editorial questions, factual corrections, source requests: [email protected].