OTHER
Archive job-site photos to S3 with AI-generated damage and progress tags
On photo upload, generates a vision description and structured tags (damage type, work stage, safety flags).
How it runs
The automated pipeline, trigger to output.
- TriggerWebhook receives photo + project IDHTTP webhook
- ActionAI vision caption + damage/stage/safety tagsOpenAI
- ActionUpload image to project-keyed S3 pathAWS S3
- ActionWrite JSON tag sidecar next to the imageAWS S3
- OutputAppend entry to searchable S3 manifestAWS S3
What it does
Builds a searchable, tagged photo archive of every site visit. Each image is described and labeled by an AI vision model, then archived to S3 with metadata you can query later for insurance, warranty, or dispute evidence.
When to use it
Use it when you need durable, searchable retention of field photos — not just storage. Useful for restoration, roofing, and inspection firms that later have to prove what a site looked like on a given date.
How it works
- 1A webhook receives the photo and its project ID.
- 2An OpenAI vision step produces a caption plus structured tags: damage type, work stage, and any visible safety hazards.
- 3The original image is uploaded to S3 under a `project-id/date/` key.
- 4A JSON sidecar with the caption, tags, GPS, and timestamp is written alongside it.
- 5A final step appends a row to a manifest object so the whole archive stays queryable by project and tag.
Set it up
What you configure once, before turning it on.
- 1Connect HTTP webhookTrigger any URL on agent actions.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect AWS S3Buckets, objects, signed URLs.
- 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.
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