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Trust infrastructure for the AI era
Your Wikipedia page is live — but it is still a single node. In 2026 people don't only check Google; they ask ChatGPT, Gemini, Claude and Perplexity, and those systems triangulate facts across Wikipedia, Wikidata, media, community and other sources. The question is no longer “do we have a page?” — it is “does the whole source network describe us correctly?”
The shift
A Wikipedia page is a strong first node. But AI answer engines never rely on one page — they cross-read a network of sources. The product is no longer the article. It is the consistency of the whole network around it.
| Yesterday's product | What you actually need now |
|---|---|
| “Do we have a Wikipedia page?” | “Does the whole source network describe us correctly?” |
| A single Wikipedia article | A consistent network of sources machines cross-read |
| A one-time publication project | A managed, recurring reputation layer |
| Page monitoring | AI-visibility & entity monitoring |
| SEO | Reputation infrastructure for search and AI |
In one line: we already built the page. The next step is making sure the entire source network around it says the same, accurate thing — to a machine and to a person.
Why it matters
You are not buying “more visibility.” You are reducing the chance of looking wrong, fragmented or invisible the moment a machine — or a buyer — checks.
Make the source graph consistent before investors, journalists or customers check it — not after an AI has already formed the answer.
The honest difference
The market is full of services promising to “force” an AI to mention you, or to “own the answer.” No one can honestly do that. We do the opposite: we make the public sources these systems read accurate and complete — through disclosed, policy-compliant work.
What we will not do
What we actually do
On compliance: paid contribution to Wikimedia projects is permitted only with proper disclosure of the paid relationship. We work through neutral sourcing, conflict-of-interest-safe requests, talk-page governance and monitoring — never undisclosed promotional editing. Editing a page about yourself directly is high-risk and frequently challenged; disclosed, source-first work is what actually survives review.
The source network AI reads
ChatGPT, Perplexity, Gemini and Google AI Overviews share a backbone of high-trust sources. Each layer below adds a distinct signal and compounds the last. We sequence them deliberately — cheapest and most foundational first — and we are candid about what each one is worth.
| Layer | What it is | Why AI leans on it | Role |
|---|---|---|---|
| Wikipedia | Encyclopedic anchor | The single most-cited source across ChatGPT, Claude and Perplexity, and a measurable share of every major model's training corpus. Only viable where notability genuinely holds — we assess first, every time. | Backbone |
| Wikidata | Machine-readable entity (Q-number) | The entity ID behind most confident AI answers and Google's Knowledge Graph. Without it you are a string of letters; with it you are a known thing AI can reason about. | Backbone |
| Wikimedia Commons | Licensed image layer | Correctly-licensed logo and imagery that feed Google's Knowledge Panel and image understanding — so the visual an AI surfaces is the one you chose. | Backbone |
| Multilingual Wikipedia / Wikidata | Per-market trust | An AI answering in German reaches first for German sources. Additional-language entities extend accurate coverage into the markets that matter — where a page is genuinely defensible. | Backbone |
| Wikipedia backlinks | Independent source density | Contextual citations inside topically-relevant articles raise the density of independent references around your entity — the corroboration both editors and AI weigh. | Reinforcement |
| Reddit & Quora | Discovery & AI-retrieval surfaces | AI systems read community discussion when forming answers. A useful, policy-safe footprint raises the chance of being represented in AI-retrieved discussion — we frame this as discovery and probability, never a guaranteed mention. | Discovery |
| Monitoring & governance | Keeps the record consistent | An open record drifts — third-party edits, stale facts, disputes. Continuous monitoring and a source-correction plan keep the whole network saying the same, accurate thing over time. | Defense |
We don't sell “presence on eight wiki farms.” The backbone is where the trust is; the other layers reinforce it. Where your sourcing supports a real Wikipedia page, that remains the strongest single signal of all.
Three packages
A real ladder, built from proven WikiBusines work rather than separate line items. Prices are per engagement, in EUR; the currency toggle shows an approximate USD. Recurring support keeps the network consistent after launch.
€2,490
+ €750 / year managed protection
one market · ~3–4 weeks
One market, base entity presence. The minimum that stops you being a single, fragile node.
Outcome
A defensible anchor and a real entity in the knowledge graph. A Google Knowledge Panel becomes eligible, and the first measurable AI-citation events begin to appear.
Most popular
€6,900
+ €1,200 / year premium support
two markets · ~5–7 weeks
B2B, two or more markets, active growth. The default recommendation — the best value-to-coverage in the stack.
Outcome
Your page becomes a source network across two markets: measurable AI-citation events over 2–3 months, a more stable Knowledge Panel, and a record that compounds instead of drifting.
€14,900
+ from €2,300 / month program retainer
multi-market · ~8–12 weeks
Fundraising, M&A, regulated industries, contested or high-scrutiny brands — where reputation is a board-level concern.
Outcome
A managed reputation operating system: a multilingual source network, an LLM knowledge layer, measured monthly reporting, and a defense plan — the version you can put in front of a board.
All work is source-first and disclosed. Every outcome above is framed as a probability we can measure, not a guarantee we can't keep — see our guarantees. Looking for the narrower, entity-accuracy-only option? See AI Visibility packages (€700–€3,500).
Which rung is yours
Step 1
The minimum that stops you being a single node — a defensible anchor and a real entity. Right when one accurate market is the goal.
Step 2
For most cases we recommend Authority: a second market, real source density, and measurement. The best value-to-coverage in the stack.
Step 3
When you are raising, selling, regulated or contested — a managed, multilingual program with monthly reporting and a defense plan.
Inside Enterprise Reputation OS
The top tier reads as logic because it is built in three layers — expansion, machine knowledge, and defense. You can also run any module on its own once Authority is in place.
English plus two additional-language Wikipedia editions where each page is defensible, multilingual Wikidata across five languages, and 20–30 contextual backlinks. Accurate coverage in every market that matters.
A source-backed knowledge hub plus the SEO & LLM Booster, with full LLM-citation measurement and monthly reporting — so the probability of an accurate AI answer is something you can see trend, not just assert.
Reddit & Quora discovery, Enterprise Governance with an SLA, a crisis source-correction playbook for when an AI generates an inaccurate or negative answer, and confidential OSINT source intelligence.
The search-results workstream
Old-school reputation management sold one promise: make negative links disappear from page one of Google. We don't sell that. Truthful coverage does not get deleted on request — by us or by anyone else. What actually moves page one, and AI answers with it, is the strength of the accurate record around you. That is the workstream, in three parts.
Workstream 1
We build and reinforce the authoritative public record — a defensible Wikipedia page, a verified Wikidata entity, consistent owned profiles, earned media. Strong accurate sources are what search engines rank and AI assistants cite, so the documented record becomes the answer people and machines actually see.
Workstream 2
Where coverage is genuinely false or defamatory, we pursue the fix where the error lives: documented correction requests to publishers, right-of-reply, and evidence-based Wikipedia talk-page work. Slower than the “burial” the old reputation market promised — and unlike it, a source-level fix propagates everywhere downstream, including AI answers.
Workstream 3
We baseline what page one of Google and the major AI assistants currently say about you, then track the same queries as the work lands. The shift shows up in a report you can check — search results and AI citations, before and after — not in a claim you have to take on faith.
If you first need to know what is actually out there — coverage, claims, weak points — start with a confidential OSINT investigation; and once the anchor page is live, Wikipedia annual support keeps it defended.
The service wrapper
The packages define the deliverables. This is the wrapper around them — who you talk to, what you receive each period, and what happens when something catches fire. It scales with the tier, and the specifics are written into the contract rather than a marketing page.
| Package | Your contact | Reporting | Crisis escalation |
|---|---|---|---|
| Foundation | A named project lead through delivery and the 90-day monitoring window | Milestone updates during the build, then alert-driven notes under Managed Protection | Email, business-hours response |
| Authority | A named project lead across both markets | Measurement reports against your AI-citation baseline, on a cadence agreed at kickoff | Priority handling under Premium support |
| Enterprise Reputation OS | A dedicated program manager — one owner for the whole program | Monthly program reporting: LLM-citation measurement, network changes, completed work | 24/7 escalation channel for active incidents; response terms are fixed in your contract SLA |
Where the record needs support beyond the wiki layer, we coordinate the adjacent channels: earned media through the media coverage service, executive social-profile upkeep through LinkedIn management, and alignment with your advertising operations so paid campaigns don't contradict the documented record. Available on any tier as add-on work; run as standing workstreams under the Enterprise retainer. We coordinate ad operations — we don't run your ads.
Start here · AI Reputation Snapshot
A one-page snapshot: what ChatGPT and Perplexity currently say about you, whether you have a Wikidata entity and Knowledge Panel, where the language and source gaps are, and which package closes the biggest one. For a deeper baseline, the full AI Visibility Audit (from €490) is credited toward your project if you proceed.
Why us
The Stack bundles services WikiBusines has run since 2010. We know which surfaces feed entity recognition, and we already manage Wikipedia, Wikidata and the source layer at scale.
15 yrs
Operating since 2010.
1,000+
Wikipedia pages a year, plus 5,000+ edits.
4,000+
Clients across 160+ language editions.
93%
Publication success rate on accepted projects.
Frequently asked questions
Where to next
Send your brand brief. We'll show you what AI says about you today, and which package closes the biggest gap — within 48 hours.