The Competitor Briefing Is the Last Manual Job in E-Commerce — and It’s Finally Getting Automated
Cross-border operators live with a structural disadvantage that domestic sellers never feel: your competitors are in a different language, a different ad ecosystem, and a different time zone, and by the time you assemble a picture of what they’re doing, the picture is stale. Most of us have accepted this as a cost of doing business — half a day of tab-hopping through Ahrefs, the Meta Ad Library, the Google Ads Transparency Center, competitor blogs, pricing pages, and Reddit threads. A new launch called Figo is trying to collapse that ritual into a weekly automated briefing, and the maker’s framing — “that half day, automatically, every week” — is worth taking seriously even if you never sign up. Here’s what’s actually new, what’s borrowed, and where I think the model breaks for sellers like us.
What Problem Figo Actually Solves (and What It Doesn’t)
The pitch from maker Ned Mehic is narrow and specific: you give it your website, it identifies your competitors from search data, and then it monitors nine surfaces per competitor — rankings and traffic, new pages, blog posts, Google Ads, Meta ads, TikTok ads, Instagram, Facebook, and Reddit mentions. On top of that, it queries ChatGPT, Claude, Gemini, and Perplexity with the questions your buyers ask, and records which businesses get named. Monday morning you get a written briefing with your numbers next to theirs and one recommended action.
Read that list again as a cross-border seller and notice what’s missing: no Amazon, no TikTok Shop product-level data, no Temu or SHEIN price scraping, no marketplace review velocity, no eBay or Etsy signal. Figo is built for the SEO agency use case — Mehic runs a “small white label SEO agency” and built it to answer a recurring client question — and that origin shows in every design choice. The nine surfaces are all web-and-social, not marketplace-and-logistics. If you sell primarily on Amazon Seller Central, this tool will tell you almost nothing about your real competitive set.
That’s not a knock on the product; it’s a positioning note. The interesting question for us is whether the architecture — weekly automated synthesis of fragmented signals into a written action — is portable to the surfaces we actually care about.
Why Amazon sellers should care more than Shopify ones
Counterintuitively, the operators who should study Figo most closely are Amazon FBA brand owners, not Shopify merchants. Here’s my reasoning: Shopify DTC operators already have a rich competitive-intel stack — SimilarWeb for traffic, Meta Ad Library for creative, Klarvio or Triple Whale for their own attribution — and the marginal value of a Figo-style briefing is convenience, not capability. Amazon sellers, by contrast, are structurally blind. You can see your own Brand Analytics search-term report and your Helium 10 or Jungle Scout estimates, but you cannot see a competitor’s ad spend, their off-Amazon funnel, their TikTok creative rotation, or whether ChatGPT recommends them when a buyer asks “best standing desk for small apartments.” Figo’s nine-surface model, transplanted to include Amazon ad transparency and marketplace page-change detection, would be a genuinely new capability rather than a nicer dashboard.
The maker’s own example briefing illustrates the gap precisely: “Flatrate Moving runs 25 Meta ads, you run 0. Open the Meta Ad Library, write down the three offers they repeat, then decide if one fits your 5.0 rating and 530 reviews.” That’s a good briefing for a local service business. For a cross-border seller, the equivalent sentence needs to reference Amazon Sponsored Brands share-of-voice, a TikTok Shop affiliate velocity delta, or a Temu price undercut — none of which Figo currently touches.
The AI-Visibility Measurement Is the Most Interesting Bet — and the Most Fragile
The part of Figo that I think every cross-border operator should be watching is the AI answer tracking. Mehic asks the same buyer questions every Monday and tracks the share of answers where each business is named, with citations counted separately. He explicitly flags this as an open question: “Nobody agrees yet on how to measure whether the assistants recommend you.”
He’s right that nobody agrees, and the reason is variance. Commenter Gal Dayan — who is building Dial, a likely AI product — put the sharpest objection on the thread: “asking ChatGPT/Claude/Gemini/Perplexity ‘who gets named’ is only as stable as those models’ answers are from one query to the next. Do you run each question multiple times per week to smooth out the variance, or is it a single snapshot? A competitor briefing that flips based on model randomness rather than an actual ranking change would be worse than not tracking it at all.”
That is the correct critique and it applies doubly to cross-border sellers. If you’re tracking AI visibility for a US buyer query, you’re already dealing with model non-determinism. Add a second language, a second locale, and a second model version rolling out regionally, and a single weekly snapshot becomes noise dressed as signal. I’d want to see, at minimum: N queries per question per week, a reported variance band, and a changelog tied to model version updates. None of that is disclosed on the launch page.
Where the math breaks
There’s a second, subtler problem with AI-visibility tracking as a KPI for e-commerce: the correlation between “named by ChatGPT” and “purchased” is unproven and probably weak for most categories. Buyers ask assistants for recommendations at the top of the funnel and then buy on Amazon, TikTok Shop, or Temu — often after checking a review video. Tracking AI mentions without a downstream attribution path gives you a metric that moves but doesn’t map to revenue. For a DTC brand with a strong branded-search halo, AI mentions might be a leading indicator. For a marketplace seller whose product page is the conversion surface, it’s closer to a vanity metric until the assistants start deep-linking to checkout — which, as of this writing, they largely don’t.
My judgment: treat AI-visibility tracking as a research input for content and positioning, not a KPI. If Figo (or a competitor) starts correlating AI mentions with branded search lift in Google Search Console or with marketplace search-term rank, that changes the calculus. Until then, the honest framing is “we measure it because it’s cheap to measure, not because we’ve proven it matters.”
What Cross-Border Sellers Can Borrow From This Architecture
Even if you never touch Figo, there are three transferable ideas here that I’d argue belong in every serious operator’s stack by the end of the year.
First: the weekly written briefing as a format. Most competitive-intel tooling gives you dashboards. Dashboards require you to know what to look for. A written briefing that says “here’s what changed, here’s your number next to theirs, here’s the one thing to do” is a fundamentally different product — it’s an opinion, not a query interface. Cross-border teams are often small and time-zone-fragmented, which makes the briefing format disproportionately valuable. You can replicate this manually today: have someone on your team write a Monday-morning Slack post with three competitor deltas and one recommended action, sourced from whatever tools you already pay for.
Second: the “nine surfaces” checklist as an audit template. Whether or not Figo monitors Amazon for you, you should be monitoring nine surfaces for your top three competitors. For a cross-border seller, my list would be: Amazon ad transparency, TikTok Shop affiliate activity, Temu/SHEIN price and SKU velocity, Meta Ad Library creative rotation, Google Ads Transparency, organic search rank on your hero keywords, new-page and blog cadence, Reddit and niche-forum sentiment, and — yes — AI assistant mentions. Figo covers seven of those nine out of the box. The two it misses are the two that matter most to marketplace sellers.
Third: the agency multi-tenant shape. Mehic asks the community whether “one account covers five businesses and each client gets a live report link” is the right shape. For cross-border operators running multiple brands or multiple marketplace storefronts, this is the right question to steal. If you run three Amazon brands across two marketplaces, you want a single pane that treats each brand as a tenant with its own competitor set and its own briefing — not one merged dashboard. Most tools force you into one-account-one-brand, which is a tax on operators who’ve scaled past a single storefront.
The agency question is the real product decision
I’d push back gently on the five-businesses-per-account default. For a white-label SEO agency, five is a reasonable tier. For a cross-border operator, five is either too few (if you run a portfolio of test brands) or irrelevant (if you run one brand deeply). The more useful axis is surfaces per competitor and query volume for AI tracking, not number of businesses. Mehic hasn’t disclosed pricing on the launch page, so I can’t evaluate the tier economics — but the shape of the question tells me the product is still optimizing for the agency buyer, not the operator buyer. That’s fine. It just means operators should expect to outgrow the default packaging quickly.
Where My Judgment Says It Falls Short
Three honest reservations, in order of how much they’d affect a cross-border seller’s decision.
Marketplace blindness is the big one. I’ve said it twice and I’ll say it a third time because it’s the whole ballgame for Amazon, TikTok Shop, Temu, SHEIN, Etsy, and eBay sellers. Figo’s nine surfaces are web-and-social. If your P&L lives on a marketplace, you are not the target user. The maker’s background — running an SEO agency serving local service businesses like moving companies — confirms this. The “real data from real moving companies” example is not a cross-border e-commerce example.
The AI-visibility metric is unproven and unstable. Covered above. I’d want to see repeated queries, variance reporting, and model-version tracking before I’d trust a weekly delta. Until then, treat AI mentions as directional.
The “one thing to do” recommendation is only as good as its category priors. A briefing that recommends “run Meta ads because your competitor runs 25” is correct for a local service business with a Meta-shaped funnel. It’s wrong for a cross-border seller whose buyer journey starts on TikTok and ends on Amazon. Recommendation engines inherit the biases of their training categories. I’d want to see Figo publish its recommendation logic, or at least let users weight surfaces by channel importance, before trusting the action item for a cross-border use case.
A note on LinkedIn and the missing surfaces
Commenter Carli Chovick asked whether Figo tracks competitor LinkedIn posts and ads. The maker didn’t answer on the thread as scraped. That gap matters less for cross-border e-commerce than for B2B, but it points at a broader issue: the nine surfaces are a fixed list, and competitive intel is a moving target. TikTok Shop affiliate activity, Temu price scraping, and marketplace ad transparency are all surfaces that didn’t exist or didn’t matter three years ago. A tool that ships a fixed nine-surface model will need a fast cadence of surface additions to stay useful. I’d want to see a public roadmap.
What I’d Watch / Test Next
If you’re a cross-border operator reading this, here’s what I’d actually do this week — no Figo subscription required.
Audit your own blind spots against the nine surfaces. Open a spreadsheet, list your top three competitors, and mark which of the nine surfaces (rankings/traffic, new pages, blog, Google Ads, Meta ads, TikTok ads, Instagram, Facebook, Reddit) you currently have any visibility into. Most operators I know score three or four out of nine. That gap is your opportunity, whether you fill it with Figo, with a VA, or with a stack of free tools.
Run your own AI-visibility snapshot — with repetition. Ask ChatGPT, Claude, Gemini, and Perplexity the five questions your buyers actually ask, three times each, on the same day. Log which brands get named. Then repeat next Monday. If the answers flip without a corresponding change in your category, you’ve just proven Gal Dayan’s variance critique on your own data. If they’re stable, you’ve found a metric worth tracking.
Watch Figo’s roadmap for marketplace surfaces. The product is early and the maker is actively soliciting feedback on the launch thread. If Amazon ad transparency, TikTok Shop, or Temu show up in the next two quarters, it becomes a serious tool for cross-border sellers. If the roadmap stays web-and-social, it stays an SEO-agency tool — useful, but not for us. I’d bookmark the Figo launch page and check back in 90 days. That’s the cheapest due diligence you’ll do all quarter.
