DATA OPS
Raw Sensor Telemetry Archive to BigQuery
Captures every incoming building sensor reading via webhook, normalizes the payload into a consistent schema.
How it runs
The automated pipeline, trigger to output.
- TriggerReceive raw sensor readingHTTP webhook
- LogicNormalize vendor payload to canonical schema
- ActionAppend row to BigQuery telemetry tableBigQuery
- OutputReturn 200 acknowledgement to gateway
What it does
This workflow is the data-residency backbone for the rest of your facilities monitoring. It accepts any sensor reading your gateways push, normalizes inconsistent vendor payloads into one flat schema (site, unit, metric, value, unit-of-measure, timestamp), and writes the row to BigQuery. Over time this table becomes the baseline that anomaly detectors compare against and the history auditors ask for.
When to use it
Use it when you have multiple sensor vendors emitting differently-shaped JSON and you want one clean, queryable history rather than scattered logs. Run it alongside your alerting workflows so detection logic has real baselines to reference.
How it works
- 1The webhook receives a sensor reading in any supported vendor shape.
- 2A logic step maps vendor-specific fields into the canonical schema and drops malformed records.
- 3The normalized row is appended to the BigQuery telemetry table.
- 4The workflow returns a 200 acknowledgement so the gateway knows the reading was stored.
Set it up
What you configure once, before turning it on.
- 1Connect HTTP webhookTrigger any URL on agent actions.
- 2Connect BigQueryDatasets, queries, schemas.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.

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