AI Brand Monitoring
AI brand monitoring uses automated collection and model-assisted review to identify relevant mentions of a brand in supplied sources. For a bounded starting point, collect public news results, distinguish genuine mentions from name collisions and prepare a source-linked digest. A news search is a selected evidence sample, not complete coverage of public discussion or social networks.
By Ai Flowi · Published
Define the brand and its false positives
Write down the exact brand name, known product names, market and ambiguous words before collecting anything. A short brand name may also name a place, person or ordinary object. Keep those exclusions explicit so a model can distinguish the subject of an article from a coincidental word match. Do not discard ambiguous mentions silently; a review category is more useful than forced certainty.
Choose what the digest should support: communications review, product feedback discovery or a dated market brief. An article criticising a whole industry is not automatically negative sentiment toward your brand. A quotation from a customer is also different from the publisher’s position. Preserve those distinctions in the output contract.
Related: Firecrawl integration for AI Flowi Workflow.
Retain the evidence behind each mention
| Proposed field | Why retain it | What to check |
|---|---|---|
| mention_reference and source_url | Connect each digest statement to its source. | The link opens the intended article, not an unrelated search result. |
| publication_date and observed_at | Separate the article date from your collection time. | Unknown publication dates stay unknown; collection does not establish recency. |
| relevance and exclusion_reason | Expose brand-name collisions and uncertain matches. | The article concerns the intended business or product. |
| sentiment_label and evidence_excerpt | Keep the classification beside its supporting language. | Identify whose viewpoint the excerpt expresses. |
| review_status | Record the reviewer acceptance or hold decision. | The model must not supply final approval on behalf of the reviewer. |
These are proposed fields for your review artifact. They are not asserted columns returned by News Search. Inspect the actual article Table, identify available source fields and record any missing metadata before defining an extraction schema. A digest that loses source links is difficult to audit even when its prose reads well.
Prepare news queries and model access
Use the News Search catalogue reference for query and locale controls before collecting the mention sample.
As an illustrative relevance check, imagine a brand name that is also a place name. An article about transport in that place is not a business mention just because its title contains the query. Require a product, company or spokesperson reference tied to the intended brand; keep uncertain matches in a separate review group. A subsidiary mention may be relevant to one communications brief and excluded from another, so write that policy into the query notes before inspecting sentiment.
For aReduce and Structured Output, configure your own model-provider account/API key and pay the provider separately. You also need a written relevance policy and someone responsible for reviewing the digest. Public availability does not justify collecting unrelated personal information. The component catalogue supplies the interface evidence used here.
Configure a news-to-digest review
example configuration, not a recorded test
- News Search: set query to the exact brand plus a disambiguating product or business term. Set hl and gl for the language and market you intend to inspect, checking the editor’s accepted values. Inspect articles for URLs, dates and the amount of text actually returned.
- Type Convert: use the record-to-Message conversion with the selected article as input_data and output_type = “Message”. Retain enough text to distinguish a quoted speaker’s opinion from the publisher’s position; a headline alone may leave sentiment unresolved.
- Structured Output: set input_value to the inspected article Message, model and provider credentials to your configured access, schema_name = BrandMentionReview and output_schema to the relevance, sentiment, date and evidence fields you require. Set system_prompt to classify only the supplied content and allow ambiguous or insufficient_evidence.
- aReduce: for a reviewed collection, set source to a Table of retained mention records, model to your configured LanguageModel and schema to the intended digest fields. Set instructions to group themes while keeping source references, uncertain mentions and conflicting viewpoints separate. Its states output is Table.
- Review the digest: open the cited articles, confirm relevance and correct attribution, then record any editorial changes outside the generated classification. The proposed design collects and prepares evidence; it does not publish replies or escalation messages.
Do not let reduction erase disagreement
The aReduce description covers collection-level processing; this digest needs an editorial grouping policy for syndicated coverage.
Group copies of the same announcement under an underlying story reference while keeping each publication link available. A commentary article responding to that announcement deserves a separate entry because it adds a viewpoint. If a digest merges both into a single positive theme, the reader loses the distinction between the brand’s statement and someone else’s judgement. Review the representative excerpts before accepting that theme, and retain disagreements that matter to the brief.
Ask the digest to distinguish recurring topics from the number of unique underlying events. Without a checked counting method, avoid turning a model summary into a quantitative sentiment report. If full article text is unavailable, the digest should state the restricted evidence level. A title, excerpt and complete article support different degrees of interpretation.
What a useful brand digest contains
The intended output lists relevant mentions with source dates, supporting passages and proposed themes. It also retains excluded matches and unresolved ambiguity so the reviewer can see what the process filtered. The digest should be readable without treating the sentiment label as the only conclusion; mixed or quoted viewpoints may require a short explanation.
Try an unrelated business with the same name, an old article newly returned by search, a headline with positive wording but critical body text, and two syndicated copies of one story. Check that the digest exposes each issue and does not claim that its source sample represents all discussion of the brand.
When should I use a different process than AI brand monitoring?
This workflow is documented in the catalogue; not production-tested by Ai Flowi. It does not establish social-network access, private community coverage, real-time alerts or reputation outcomes. WhatsApp, Messenger and Instagram monitoring are not covered here. If the requirement is a comprehensive media archive, first assess a service that can demonstrate the required source access and retention.
For a small set of articles, manual review may be enough. Model classification can misread sarcasm, quotations and local context. Keep sensitive escalation decisions with a communications reviewer, and budget for source collection and model requests as separate operations.
Plans: Go from USD8/month billed yearly (USD10 monthly), Plus from USD16 (USD20 monthly), Pro 100 from USD80 (USD100 monthly), Pro 200 from USD160 (USD200 monthly) and Pro 500 from USD400 (USD500 monthly), with 5,000 to 250,000 Flowi Tokens/month and top-ups; 1 token per node run; third-party API and model usage is not included.
Related: Competitor Price Monitoring.
Keep brand evidence separate from strategy
Use competitive intelligence to interpret reviewed market changes, and marketing automations to prepare approved content from those observations. The use-case hub describes research starters with different deliverables.
Related: AI keyword research: cluster supplied demand without inventing volumes.