I’ve been running DTC campaigns across both markets for about two years now, and I just went through a painful exercise that I think deserves a real conversation here.
We built a workflow to discover and vet creators across Russian and US audiences using AI-powered tools. On paper, it looked solid—the system flagged engagement rates, audience demographics, content consistency, all the usual signals. But when I started actually reaching out to the creators the AI recommended, something felt off.
I met with three Russian creators who had perfect scores in our vetting system. All three had legitimate followings, authentic engagement, and their content aligned with our brand values. But here’s what the AI couldn’t catch: one creator had just come off a controversial sponsorship deal (it was handled badly, and the community was still salty about it). Another was in the middle of a quiet dispute with another brand in our category. The third—the AI loved this person—had incredible metrics, but when I actually talked to them, they were clearly burned out and just shipping content on autopilot.
None of these red flags showed up in the fraud detection or the vetting scores. They weren’t engagement farms. They weren’t breaking any rules. The AI just… couldn’t see the human context.
So we started adding a manual review step where someone from our team actually digs into each creator’s recent history—not just the metrics. We look at their comment sections, how they respond to criticism, whether they’re actually invested in the partnerships they do. It’s slower. It adds work. But it’s caught mismatches that the AI would have pushed us into.
I’m not saying AI discovery is broken. The speed and scale it gives us is real. But I’m starting to think of it less as “find the best creators” and more as “eliminate the obvious red flags quickly.” The actual vetting? That’s still a human job.
Has anyone else hit this wall where the AI scores look perfect but something’s still off about the creator? How are you actually validating before you commit budget?
Этот пост резонирует со мной на 100%! Я всё время говорю своим клиентам: AI—это фильтр, а не советник. Он отсеивает явный спам и подделки, но настоящая работа—это знакомство с людьми.
Когда я вижу создателей с идеальными метриками, я всегда начинаю с личного разговора. Я смотрю на их DM, как они общаются, готовы ли они слушать. Потому что AI не может измерить энтузиазм или способность адаптироваться к бренду.
Мне нравится, что вы добавили ручной шаг. Это экономит кучу проблем дальше по пути. Может, стоит поделиться, как вы структурировали этот процесс проверки? Я думаю, многие здесь хотели бы знать, на что конкретно вы смотрите при углубленном анализе.
Кстати, я часто рекомендую создателям быть открытыми с брендами о том, через что они проходят. Если кто-то восстанавливается после неудачной коллаборации, честность об этом на самом деле создаёт доверие. Может быть, это тоже сигнал, который бренды должны ценить больше?
Спасибо за этот пост—это прямо в точку. Мы в нашем стартапе столкнулись с похожей проблемой. Пытались масштабировать на европейский рынок, положились на AI для поиска партнеров. Результат? Потратили деньги на людей, которые выглядели идеально на бумаге, но в реальности были либо недоступны, либо не заинтересованы.
Теперь я понимаю, что AI хорош для первого слоя отсева, но для действительно критичных партнерств нам нужно делать домашнюю работу. Разговаривать, проверять рекомендации, смотреть, как они работают с другими брендами.
Вопрос: как вы подходите к проверке в международном контексте? Есть ли культурные различия в том, как вы оцениваете создателей в России vs. США? Потому что то, что выглядит как красный флаг в одной культуре, может быть нормой в другой.
This is exactly why we invested in hybrid workflows at our agency. We run the AI discovery phase—it’s efficient, it scales—but then we have an experienced person on the team who does the relationship audit. It costs more upfront, but it saves us from costly mistakes downstream.
The clients who buy blind on AI scores alone? They’re the ones who come back with disappointed feedback or campaign underperformance. The ones who invest in the vetting step see better results and, frankly, stronger creator relationships long-term.
One thing we’ve started doing: after vetting, we actually teach creators how we evaluate partnerships. Transparency builds better collaborations. Creators aren’t just slots to fill; they’re partners. When they understand our criteria, they self-select better.
Have you considered sharing your vetting criteria with creators upfront? We found it actually attracts more serious artists and filters out the transactional types.
Okay, from the creator side—thank you for this. So many times I get pitch emails that feel completely automated, and you can tell the brand never actually looked at my account. They’re just spamming everyone with a certain follower count.
When a brand actually knows my content and my vibe, the collaboration is so much better. I can tell the difference between “AI picked you from a list” and “we actually thought you’d be a good fit.”
Your manual vetting step? That’s what separates the brands I want to work with from the ones I’m just taking money from. And yeah, I’ve had rough collabs too. I’ve learned which brands align with my values and which are just looking to extract content.
Maybe creators should also have a vetting process? Like, how do we know a brand is actually legit and won’t destroy our reputation with a bad campaign?
This is a solid operational insight. You’re essentially describing the limitations of algorithmic vetting at the creator-brand fit layer. A few thoughts:
-
Signal decay: AI models train on historical data. Social media moves fast. A creator can shift their audience, their engagement model, or their brand alignment in weeks. Your manual step captures real-time context.
-
Hidden factors: Reputational risk, creator burnout, competitive conflicts—these are qualitative variables that require human judgment. AI can flag engagement anomalies, but it can’t assess “burning bridges with a brand.”
-
ROI structure: The question isn’t whether manual vetting slows you down; it’s whether it improves campaign performance enough to justify the time cost. Have you calculated the ROI difference between AI-only vetting and hybrid?
One suggestion: Consider building a mini-scorecard for the human review layer. What specific variables are you actually evaluating? If you can codify those, you might be able to eventually train a secondary model to flag them automatically.
How are you currently measuring whether the hybrid workflow is actually outperforming baseline?