Everyone who has ever hired someone using LinkedIn as the primary research tool has hired the wrong person at least once. I know I have.
The Hire That Made Me Build Something
Three years ago I brought on a VP of Growth who looked perfect on paper. Consistent logos, credible titles, a feed full of engagement posts about pipeline velocity and demand gen frameworks. Six months later we were untangling a mess: the strategy he had championed at his previous company had actually been built by the team he managed, not him. His public presence had packaged someone else's output as his own thought leadership, and I had no tool sharp enough to see through the packaging. I paid for that blind spot in runway and time I cannot get back.
That experience pushed me to ask a question I had not asked seriously before. What does a person actually believe, say, and argue for over time - not what their profile claims they did? LinkedIn is a resume. A resume is a sales document. When I am evaluating whether someone belongs inside my company, on my cap table, or on my advisory board, I do not want the sales document. I want the unedited record.
The problem is that the unedited record is scattered. It lives in podcast transcripts, conference talk recordings, old tweets, Substack newsletters, Reddit threads, GitHub commit messages, and occasionally a blog post from 2019 that accidentally reveals exactly how someone thinks under pressure. Stitching that together manually for one person takes hours. Comparing two or three candidates in parallel is essentially impossible without a system.
What I Actually Built and Why
When I started spec-ing out what became MentionFox, the compare feature was not even in the original roadmap. I was thinking about brand monitoring and AI-generated mentions - watching how large language models talk about companies. But then my co-founder and I started using our own early prototype to research potential angel investors, and something obvious clicked. We kept running the same query twice and manually diffing the results. We needed a side-by-side view.
So I built candidate evaluation as a dedicated workflow rather than a bolt-on. The core idea is simple. You input two or more people - founders, executives, investors, candidates, advisors - and the platform pulls their public signal across sources, then surfaces a comparative breakdown of what they have actually said, how they have positioned themselves, where their stated views contradict each other or their claimed track records, and how they appear in AI-generated responses about their space.
That last part matters more than most people expect.
What the Data Taught Me That Surprised Me
The first time I ran a real comparison between two B2B SaaS founders I was considering for a board seat, I expected the tool to mostly confirm what I already thought I knew. It did not. The founder with the shinier LinkedIn profile and the cleaner narrative had almost no footprint in the places where actual practitioners have hard conversations. No niche forums. No technical writing. No podcast appearances where the host asked uncomfortable questions. His content was entirely top-of-funnel personal branding.
The other founder was messier. Fewer followers. A LinkedIn profile that read like it had been updated by someone who finds writing bios mildly embarrassing. But she had three years of substantive commentary in a Slack community for B2B operators, a handful of conference talks where she openly discussed a product decision that failed, and she showed up consistently in AI-generated answers to specific questions about category creation in her space. That AI visibility signal was particularly telling because it means the model has ingested enough of her genuine thinking to represent her perspective. That takes volume and consistency, not just a good headshot.
I gave her the board seat. It has been eighteen months. I was right.
The other finding that reshaped how I use the tool was about contradiction detection. I was evaluating two candidates for a head of sales role. Both claimed to be product-led growth converts who believed in bottom-up adoption. When I ran them through the comparison, one of them had written extensively in 2021 about why PLG was a fad and enterprise top-down sales would always dominate. He had never publicly updated that view. That is not a disqualifier by itself - people are allowed to change their minds. But he had not said he changed his mind. He had quietly rebranded. That gap between the old record and the current positioning told me something about how he handles information that makes him look wrong.
The other candidate had a different kind of inconsistency. She had been vocal about PLG since 2020, but her language had evolved visibly. I could watch the thinking sharpen in real time through her public writing. That kind of inconsistency - the kind that shows someone actually absorbing new information - is the kind I want to hire.
How I Use It Now Week to Week
I run comparisons in three contexts now, not just hiring.
For investor research, I use it before any pitch meeting. I want to know whether a partner has actually engaged with companies in my category before or whether they are pattern-matching from a distance. Public signal tells me how they think about the problems I am solving.
For competitive intelligence, I compare the founders of companies I compete with. What are they saying publicly about the market? Where are they signaling product direction through their content choices? This is not about spying. It is about understanding the intellectual frameworks of the people building the things that might obsolete me.
For hiring, the workflow is now: job posted, applications in, top candidates compared against each other using the tool before I ever schedule a first call. I go into screening calls knowing things the candidates did not realize I would know. Not to ambush anyone - to have a real conversation instead of a rehearsed one.
What to Do If You Want to Try This Yourself
If you want to run your own version of this manually before committing to a tool, start with one constraint. Pick one substantive topic that would be directly relevant to the role or relationship and search for what the person has actually said about it outside their profile. Not what they listed as a skill. Not what their summary claims. What they argued, in their own words, in a context where they were not obviously trying to impress you.
Then do it for the second person and compare. What you are looking for is not who sounds more polished. You are looking for whose thinking you would trust when the situation is ambiguous and there is no right answer on the slide deck.
If you are comparing more than two people, or doing this more than occasionally, doing it manually will break you. The surface area is too large and the signal is too buried.
MentionFox pricing is structured for teams that are doing this kind of research regularly - not just occasionally Googling someone before a call. If you are making decisions where the cost of a bad pick is six months of runway or a board dynamic that does not work, the comparison workflow starts paying for itself on the first decision it sharpens.
If you want to see how MentionFox handles side-by-side candidate evaluation, that is the place to start. It is the feature I built for myself first, which is usually a good sign.
If you found this useful, I write about solo-founder distribution, B2B SaaS, and what's actually working in the AI-search era over on my Substack (one post per week, no spam).
I'm building MentionFox - a B2B intelligence suite that combines brand mention tracking with AI-visibility (GEO) measurement, investor research, and outreach automation. There's a free tier and a 5-day trial of Pro at mentionfox.com/pricing.
Top comments (0)