Renewable energy / solar-as-a-service

A Three-Front Reputation Correction for a European Solar Company

Search suppression, a Wikipedia rewrite and Autocomplete correction run together for a European solar company, with assistant answers tracked in both operating languages.

85% Updated Outcome (change in inaccurate/outdated coverage)
4 Weeks Time to first confirmed edit or correction
11 Months Total engagement duration

Impact at a Glance

Wikipedia article accuracy

Before Outdated, unsourced
→
After Bias neutral, facts updated

Google Knowledge Panel

Before Mirrored the article
→
After Facts now current

AI assistant answers

Before Repeated old claims
→
After 75% corrected

The Challenge

A European solar-as-a-service company was fighting the problem on three fronts at once. Search results for the company name surfaced complaint-site content ahead of anything the company controlled; Google Autocomplete was suggesting negative, high-volume completions the moment someone started typing the company's name; and the Wikipedia article, where one existed, echoed the same complaints without context or resolution. Each channel reinforced the others — a prospective customer who saw a negative autocomplete suggestion, then negative search results, then an unflattering Wikipedia mention, had three separate reasons to distrust the company before reaching its own website. The company needed all three fixed in a coordinated way, not one at a time, and needed the correction to hold up when the same questions were put to an AI assistant instead of a search bar.

Our Solution

Search suppression, a Wikipedia rewrite and Autocomplete correction run together for a European solar company, with assistant answers tracked in both operating languages.

  1. Neutrality Restoration

    Rewrote the founding, leadership and service-model sections from independent sources, neutralizing a slant that appeared to have been introduced by competitors rather than by ordinary editing.

  2. Editor Network Escalation

    Routed the corrections through independent editors, who picked their approach according to how contested the page was at the time — direct edit, Talk-page discussion, or advance notice.

  3. Cross-Property Panel Alignment

    Reconciled the panel against the article, the company's owned properties, third-party databases and linked Wikidata entries, so every source feeding the panel agreed.

  4. Bilingual Assistant Monitoring

    Tracked assistant answers in both languages the company operates in, since a correction that lands in one language routinely fails to carry into the other.

  5. Autocomplete and Suppression Push

    Built and promoted credible owned and earned content to displace stale negative coverage, and worked the signals behind Autocomplete until suggestions stopped leading with complaint terms.

The Results

Engagement milestones are listed below.

Day 14

Audit closed across all three fronts — results, Autocomplete suggestions and the article itself.

Month 1–2

First sourced edits with editors; suppression content published and Autocomplete signals worked in parallel.

Month 3–4

Article neutral and stable. Panel matched the company's current reality across every feeding source.

Month 6+

Two-language monitoring running, covering reversions on the article and drift in assistant answers.

Key Takeaways

  • Three channels reinforcing each other have to be worked together. Fixing one leaves the other two still supplying the original impression.
  • Autocomplete shapes the search before the searcher finishes typing, which makes it the first impression rather than a secondary one.
  • A slant introduced by competitors is corrected with sourcing, not counter-argument. Neutrality is the standard Wikipedia enforces.
  • Corrections do not cross languages on their own. Multilingual markets need multilingual monitoring.

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