Use case · brand and mention monitoring
Tracking mentions of a brand or competitor: what we run, what it costs, where it falls short.
Tracking a brand's mentions works when the noise is cut before a model is paid to read it. On our own instance only 27% of the items judged for a topic reached a feed. This guide gives the numbers, the setup, the cost and the limits.
By the feedql team · Updated 15 October 2026 · Figures from our own running instance, 7 days to 14 October 2026
- Worth it when mentions arrive faster than you can read them. With a couple of mentions a month, a weekly digest is enough.
- Works today on the web, YouTube, Reddit, X, Hacker News, Medium, GitHub, app stores, forums and the Telegram channels you add.
- Coming soon: search across all public Telegram channels. See Telegram mentions.
What do the numbers on our own instance show?
These come from the instance we run every day: 26 topics, 22 source types, two testers besides us. They are volumes and shares, not customer data. The queries are kept so we can refresh them.
- 30,130items published in the 7 days to 14 Oct 2026, across 22 source types
- 53,460events formed in the same week; 1,503 of them (2.8%) hold two or more reports
- 27%of the 125,326 items judged for a topic reached a feed; 41% were stopped by the meaning pre-gate without a model call
- $0.44model cost of 2,947 scoring calls in 7 days (Haiku 5.5)
| Name | Count | Share |
|---|---|---|
| 10,122 | 33.6% | |
| GitHub | 6,366 | 21.1% |
| App Store reviews | 5,546 | 18.4% |
| Google Play reviews | 2,291 | 7.6% |
| X | 1,764 | 5.9% |
| Hacker News | 849 | 2.8% |
| YouTube | 732 | 2.4% |
| Websites and blogs | 462 | 1.5% |
| Forums | 418 | 1.4% |
| Telegram | 201 | 0.7% |
| Name | Count | Share |
|---|---|---|
| Judged for a topic | 125,326 | 100% |
| Stopped: none of the topic's words | 38,865 | 31.0% |
| Stopped: far from the topic by meaning (pre-gate) | 51,566 | 41.1% |
| Stopped: scored below the threshold | 6,890 | 5.5% |
| Stopped: exclusion word, language or similarity to a down-voted item | 776 | 0.6% |
| Reached a feed | 34,079 | 27.2% |
The stage counters are incremented at different points, so they add up to about 5% more than the judged total. Read them as shares, not an exact split. We do not measure the time from a post to its row in the feed, so we give no latency figure. Model cost is the list price of the scoring calls recorded in that week; collecting, proxies and scraping are not included.
What this says for mentions: most of what a source returns is not about your topic. Roughly three quarters of judged items never reached a feed, and the two cheapest gates (the words and the meaning pre-gate) did most of that work before any model was paid for.
How does it work?
A mention is any post, video, review or article that names your brand. The work is not finding them once. It is finding them every day without ten copies of the same one, and without the namesakes.
1. Write the keywords
Describe the brand in plain words and feedql drafts a keyword pack you can edit: names, spellings, phrases, up to 500 entries. Whole words match with one ending ignored; a trailing * matches a prefix.
2. Pick the sources
Keywords are searched on the sources that support search. Telegram channels and sites are followed by link.
3. Collect and fold
Each source is read on a schedule it learns from how often it posts. Reprints fold into one entry and reports of the same story are grouped, with the first source linked.
4. Score with a reason
Every mention that passes the cheap gates gets a 0-100 score against your brief and a reason of up to ten words.
5. Get it where you work
Read the feed, export a table, receive a scheduled digest or a webhook, or let your agent pull it through the API and MCP.
How do we set keywords, exclusions and must-see words?
The setup that has worked for us, in the order we do it.
- Start from how people write the name, not how you do. Include misspellings, the domain, the app name and the product versions people mention. Check the test run: it shows scored results by source before you follow anything.
- Add exclusions for namesakes first. Exclusion words are applied before any AI spend, so they are the cheapest filter. Exclude whole phrases, such as a salon or a band that shares your name.
- Use must-see words sparingly. A must-see entry always shows, but the post has to be about the term: in the title, or at least twice in the first 600 characters. A passing mention is judged like any other post.
- Pick a match mode.
wordsis the default;phraseandpartsare stricter or looser;embeddrops the word gate and lets the meaning pre-gate decide, which suits brands people describe without naming. - Read the reason line, then set the threshold. Each item says in up to ten words why it scored as it did. If the feed looks empty, the funnel shows which gate stopped the items, and you can lower the threshold.
What does a result look like?
One row per story, newest first. The example below is made up: the brand, the posts and the links are invented to show the shape of the result.
| Date (UTC) | Source | Link | Excerpt | Score |
|---|---|---|---|---|
| 2026-10-14 09:12 | Reddit · r/smallbusiness | reddit.com/r/smallbusiness/… | “Moved our invoicing to Lumenly last month. The import was painless, but reports crawl on big accounts.” | 91 |
| 2026-10-14 07:40 | YouTube · “Invoicing apps compared” | youtube.com/watch?v=… | “…Lumenly is the cheapest of the four, but support only answers by email.” (from the captions, at 6:42) | 84 |
| 2026-10-13 18:05 | Hacker News | news.ycombinator.com/item?id=… | Show HN: Lumenly 2.0 adds multi-currency invoices. 4 reports of this story, grouped. | 78 |
| 2026-10-13 12:30 | Website · a software review blog | example-reviews.dev/lumenly… | “Lumenly review: pricing, limits and what is still missing after a year of use.” | 72 |
| 2026-10-12 21:18 | Telegram · a channel you added | t.me/…/1204 | “Has anyone moved from Lumenly to Billfold? Looking at the export options.” | 69 |
The export has more columns: the title, our summary, the reason for the score, the story a mention belongs to, language and topic. Full article text is never included.
What went wrong for us?
Three things we got wrong and what we changed.
- A buzzword flooded a topic (11 October 2026). Every post that merely mentioned one product name was forced to a score of 100 in a topic about something else. A must-see entry is now a guarantee of visibility, not a score: the post must be about the term, the scorer still ranks it, and settings warn when most hits are passing mentions.
- Issue search drowned a topic in bug reports. GitHub keyword search now reads repositories only unless a topic asks for issues.
- Keyword matching alone is noisy. About 31% of what our sources returned contained none of the topic's words, and another 41% was off topic by meaning. That is why every mention carries a score and a reason, and why the pre-gate exists.
Which sources work today?
The state of each source. For Telegram specifically, see mentions in Telegram channels.
- Live Web search and articles Pages found through search engines; a few searches a day per keyword set
- Live Websites and blogs you add Feeds, sitemaps and HTML, learned once per site
- Live YouTube Searched by keyword; captions are read when the video has them
- Live Reddit Searched by keyword
- Live X Searched by keyword
- Live Hacker News Stories and comments
- Live Medium Stories by keyword, tag or author
- Live GitHub Repositories, issues and releases
- Live Telegram channels you add Public channels, followed by link
- Live App Store and Google Play Newest reviews of apps you name
- Live Forums and Stack Exchange Discourse forums and Q&A sites
- Coming soon Search across all public Telegram channels Being built. Today a channel has to be added by its link.
What are the limits and the cost?
What to know before you rely on it.
- It is not real time. Each source is read on a learned interval, from every 10 minutes for busy social sources up to once a day for quiet ones; app stores are slower.
- Web search is the costly source. Pages are found through search engines in real phone browsers, so a keyword set gets at most 12 engine queries a day.
- Scoring costs money. Collecting is not metered. The model that scores and explains is, and each topic shows its own spend. Our scoring ran at $0.44 for 2,947 calls in the week above.
- Exports are capped. Three a day, 200,000 rows each, and they carry a summary and an excerpt, never the full article text.
How do you receive the mentions?
The same data feeds the web feed, a spreadsheet, your inbox and your agents.
Feed on the web
Newest and best first, grouped by story, each row with its reason and a link to the original.
Table export
CSV or JSONL of everything a query finds: date, source, link, title, excerpt, our summary, score and reason. Up to 3 exports a day, 200,000 rows each, a download link valid for 24 hours.
Digest and webhook
A written digest on a daily, weekly or monthly schedule, in your time zone. A signed webhook pushes each new mention to your own system as it is scored.
API and MCP
REST API and an MCP server, so Claude or your own agent can search the mentions and manage the keyword pack.
When is it worth it, and when not?
Our position, so you can decide quickly.
- Worth it: a product or a competitor that is discussed in many places at once, where the same story is reprinted and you need the first source and a reason to read.
- Worth it: you want the mentions as data: a table, a webhook, or something your agent can query.
- Not worth it: a tiny brand with a couple of mentions a month. A weekly digest does the job without a live feed.
- Not the tool: you need every post on a closed network, or a real-time alert within minutes.
Frequently asked questions
Can feedql search all of Telegram for my keywords?
Not yet. Today it reads public Telegram channels that you add by link and applies your keywords to what they post. Search across all public channels is coming soon.
Can I get the mentions as a table?
Yes. Export CSV or JSONL with the date, source, link, title, excerpt, summary, score and reason. The export never includes full article text, only the link, our summary and a short excerpt.
How soon do I see results?
A test run before you follow a topic shows scored results from the sources you pick in about three minutes. After that the feed fills as the sources are read.
How does it tell my brand from an everyday word?
Three layers: exclusion words drop known namesakes before any model runs, must-see words keep posts that are really about your name, and the score says how relevant a mention is and why.
Can an AI agent read the mentions?
Yes. The same data is available through an MCP server and a REST API.
Can I use it now?
feedql is in private beta and opens by invitation. Join the waitlist below and say what you want to track.
Tell us what to watch
The first seats go to a small group. Leave your email and name the brand or product you want to track.