This article was AI-generated as part of an experimental historical-content project. The date reflects the period being analyzed rather than the date the article was originally written.
In December I suggested a year-end audit: re-baseline search results, check AI Overviews in local languages, ask ChatGPT search the same questions, read Wikipedia line by line, look for name collisions. That list still works. But the first quarter of 2025 added enough new ways for people, and software, to encounter a company that it needs a spring revision.
The changes, briefly. DeepSeek released R1 in January as an open model under the MIT license. OpenAI launched Operator, an agent that uses its own browser to complete tasks, and then deep research, which produces long cited reports. Google opened AI Overviews to teens and signed-out users and began testing AI Mode. OpenAI gave developers built-in web search and computer use through its new Responses API. And yesterday Anthropic turned on web search for paid Claude users in the U.S.
Here is what I would add.
Run one question set across every answer surface
Pick ten to fifteen questions a stranger would ask: who runs the company, is it legitimate, what happened in its best-known controversy, how it compares with its two main competitors. Run them in Google signed out, so the AI Overview appears as it does for strangers, then in ChatGPT search, Perplexity and Claude with web search on. If anyone on the team has AI Mode access through Google One AI Premium, include it. Record which sources each one cites. The useful finding is often not a wrong answer but a source you did not expect to matter, which is why cited answers deserve their own review.
Commission a research report on yourself
Ask a research agent for a report on the company, framed the way an analyst or journalist would frame it. A long document with footnotes reads like due diligence, and recipients will treat it that way. OpenAI says deep research “may struggle with distinguishing authoritative information from rumors.” Better to learn which rumors it picks up before someone else’s report starts circulating.
Walk a task path the way an agent would
Agents act on what they find. Give one an ordinary task involving your company: find the support contact, check a return policy, locate the investor relations page. Watch where it gets confused, lands on an unofficial site or relies on an outdated page. An agent that contacts or pays the wrong party creates a customer problem with your name attached.
Assume open models will describe you where you cannot see
R1’s weights are public, so it can run inside apps, internal tools and hosting services you will never monitor. Developers can now add web search to their own applications with a few lines of code. You cannot audit all of these. You can make sure the facts most likely to be retrieved, on your own site and in authoritative independent sources, are consistent and current.
Check facts as components, not paragraphs
Google describes AI Mode as breaking a question into many related searches, and research agents work in a similar way. That rewards facts that stand alone clearly: founding date, headquarters, leadership, what a past incident was and how it was resolved. Read your Wikipedia and Wikidata entries with that in mind, and correct errors through the proper channels, with disclosed affiliation and independent sources.
Search executives’ names on their own
Name collisions were on the December list, and they are worth repeating. A research agent is less likely than a person to notice that the executive named in a lawsuit is someone else with the same name.
Re-baseline when the core update finishes
Google said the rollout could take up to two weeks from March 13. Once it completes, capture fresh snapshots and note the type of source in each position, not just the ranking.
The common thread is that more of the first impression now forms inside systems acting on someone’s behalf, sometimes without a person reading the results at all. An audit used to mean looking at the company the way a stranger would. This spring it also means looking at it the way a stranger’s software would.