MARKET RESEARCH
Trend Term History Loader to BigQuery
Captures the day's Brave Search snapshot for your tracked terms, normalizes and clusters them.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule starts the load
- ActionPull Brave Search snapshot per tracked termBrave Search
- ActionNormalize terms and assign cluster idsOpenAI
- LogicShape records to warehouse row schema
- OutputAppend rows to BigQuery trend-history tableBigQuery
What it does
This workflow turns daily search signal into durable data. It pulls Brave Search results for every tracked term, has OpenAI normalize and tag each into a theme cluster, and appends timestamped rows to a BigQuery table so analysts can chart emergence curves and run their own SQL over months of trend history.
When to use it
Use it when a one-off brief isn't enough and you need a warehouse-backed dataset — for dashboards, cohort comparisons, or feeding downstream models. Best for data and analytics teams who own a BigQuery instance.
How it works
- 1A daily schedule starts the load.
- 2Brave Search returns result counts, top domains, and snippets per tracked term.
- 3An OpenAI step normalizes raw terms and assigns each to a theme cluster with a stable cluster id.
- 4A logic step shapes the records into the warehouse row schema and stamps the run date.
- 5A BigQuery step appends the rows to the trend-history table for querying.
Set it up
What you configure once, before turning it on.
- 1Connect Brave SearchWeb, news, image, video search.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect BigQueryDatasets, queries, schemas.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.