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The fastest read on what a consumer category is being told this month.
Free to try, no card. Your TikTok video opens in Ancher.
Paste your TikTok video. Nothing to upload.
Every one of these stays separately addressable instead of collapsing into one block of text.
A structured read on a market: who is in it, what buyers say, how it is priced, where the gaps are.
Nothing moves faster than this platform, which makes it the earliest place a product claim, a price point or a complaint shows up in the words buyers actually use. Ancher counts claims across distinct creators, keeps prices from on-screen text with the post date, and states that view counts are not demand and that the comments — where corrections live — were not captured.
The honest version
A claim can go from one creator to a hundred in a week here, and the mechanism that spreads it is the same whether it is true or not. That makes this the best early-warning system on the matrix and the worst evidence base. The report is built to be read as a signal that something is being said, not as a finding that it is so.
One second of one video, at 0:44, read two ways.Illustrative. The figure is invented; the gap it falls into is not.
everyone is switching and it's purely because of the price, it's
On screen in the video — not readThe specific never reaches the draft
Lands in the draft, with its timecode
What comes out
A structured read on a market: who is in it, what buyers say, how it is priced, where the gaps are.
The category as creators describe it to buyers, in the vocabulary the audience repeats back.
Recurring claims and complaints, counted by distinct creator.
Products named, split by whether a creator used one or listed it.
Prices from on-screen text, each with the video's date.
Complaints recurring across unrelated creators that nothing named answers.
The link on its own gets a summary. This is the instruction that produces the 5 sections above, in that order, with the rules that keep them honest. Paste it with your TikTok videos — in Ancher, or in whatever assistant you already use.
Build market research from these videos. (1) Define the category in creators' own words. (2) List recurring claims and complaints, counted by distinct creator and never weighted by views. (3) Name every product, marking used or listed. (4) Record prices from on-screen text with video dates. (5) List unanswered gaps. State on the face of the report that it records what is being claimed rather than what is true, that views are not demand, and that comments were not captured.
The detail, if you want it
| From the TikTok video | Into | Why |
|---|---|---|
| Audio transcribed with timings | Demand signals | The complaint is spoken and exists nowhere else on the page, so the audio is the only route to what buyers are actually saying. |
| Frames from the video | Pricing | Prices are typed on screen rather than spoken, which is precisely what a caption-based read returns nothing for. |
| Caption and hashtags | Market definition | Hashtags are a blunt but honest statement of the category as the creator understands it. |
| Creator handle | Demand signals | The same claim from unrelated creators is a signal; the same claim from one network is a trend being manufactured. |
| Likes, comments, shares | Opportunities | Share count separates a complaint people recognised from one that merely played, which is the closest thing to recognition here. |
Almost everything of value in a TikTok is spoken, and nowhere else on the page.
On-screen captions and product shots are recoverable only from the frames themselves.
Hashtags are a blunt but honest statement of who the creator thinks the audience is.
Share count separates a format that travelled from one that merely got watched.
Attribution matters more here than anywhere, because reuse without credit gets noticed fast.
No, and the report says so on its face. What it gives you is the earliest available record of what a category is being told and in what words, which is a positioning input rather than a sizing one.
Imperfectly. The report counts distinct creators and flags clustering in time, which is the honest limit of what it can see. Disclosure is unreliable, so the flag is a prompt to check manually rather than a verdict.
Because the same mechanism produces both. You will see a claim here weeks before anywhere else, and you will see false claims on exactly the same timeline with exactly the same reach.
Everything you save lives in one workspace, so the market research report is built from your sources — not from a model's memory of the internet.
Open Ancher →Not a TikTok video? The same market research report also comes from YouTube video, PDF, X thread.