MARKET RESEARCH

Brave Search Spike Detector with Airtable Trend Tracker

Daily, measures result volume per tracked keyword on Brave Search, logs the count to Airtable, and flags any keyword whose volume spikes above its trailing baseline.

CategoryMarket Research
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily cron
  • ActionPull result volume and top hits per keyword from Brave SearchBraveBrave Search
  • ActionRead trailing baseline per keyword from AirtableAirtableAirtable
  • LogicCompare today vs baseline, tag spikes
  • ActionWrite daily snapshot to Airtable historyAirtableAirtable
  • OutputAlert Slack on detected spikesSlack

What it does

Tracks how loud each of your keywords is on Brave Search day over day. It records a daily result-volume snapshot per keyword in an Airtable base, compares today's count against the keyword's recent average, and only raises an alert when something breaks out of its normal range. Quiet days stay quiet; real spikes surface immediately.

When to use it

When you want an early-warning system for emerging interest in specific terms — a competitor name, a feature category, a regulation, a product defect phrase. Ideal for analysts who already live in Airtable and want a queryable history rather than a one-shot report.

How it works

  1. 1A daily cron triggers the sweep.
  2. 2Brave Search returns result counts and top hits for each tracked keyword.
  3. 3The flow reads each keyword's trailing baseline from Airtable.
  4. 4A logic step compares today's volume to the baseline and tags spikes.
  5. 5Today's snapshot is written back to Airtable to extend the history.
  6. 6If any keyword spiked, a Slack alert names the keyword, the jump, and the leading sources.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect Brave SearchWeb, news, image, video search.
  2. 2
    Connect AirtableBases, tables, views, automations.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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