Data handling & retention

What happens to the files you upload, where they are processed, how long anything is kept, and what is — and is not — used for AI training. All figures on this page describe the production platform as currently deployed.

Retention at a glance

Data	Kept for	Notes
Uploaded input files	~1 hour after upload	A cleanup job runs every 15 minutes. Files attached to a workflow that is still running are kept until it finishes, then cleaned up. A 7-day storage lifecycle rule acts as a backstop.
Output files (results)	30 days	Produced spreadsheets, documents, reports and other artifacts.
Download links	1 hour	Result download URLs are pre-signed and expire; request a fresh one at any time while the artifact exists.
Workflow execution state	7 days	The agent's internal checkpoint (used to resume interrupted runs) auto-expires.

Delete your files immediately

You don't have to wait for automatic cleanup. API/SDK callers can delete an uploaded file's cloud copy — the storage object and its metadata record — the moment a workflow finishes:

client.files.delete(file_id)          # Python SDK (async: await client.files.delete)

or DELETE /api/v1/files/{file_id} directly. Only the uploader can delete a file, and a file still attached to a running workflow is protected until the run reaches a terminal state.

Where your data is processed

Step	Service & region
File storage & workflow execution	AWS, Tokyo (ap-northeast-1)
AI model inference	AWS Bedrock, N. Virginia (us-east-1)
OCR (text recognition in images/PDFs)	Amazon Textract, Singapore (ap-southeast-1)

The processing region is a property of the platform deployment and is not currently selectable per request. If your compliance posture requires a specific data-residency arrangement, contact us.

This table describes how processing works in practice. The

sub-processor list on the product site is the authoritative one — if the two ever differ, that page governs.

AI training

Model providers do not train on your data. Convilyn runs AI inference through AWS Bedrock; Anthropic does not train its models on inputs or outputs sent through Bedrock, and your content is not used to improve any third-party model.
Content from the live service is not used to train models — ours or anyone else's. Whether it arrives through the web app or an API key, it is never added to a training dataset. The tool that builds Convilyn's internal training sets reads development data only and refuses production databases in code, so this does not rest on policy alone.
Requests you type in the first-party web app may be used, after identifying details are removed, to evaluate how accurately requests are matched to workflows. The scope, limits, and retention of this use are defined only by the Privacy Policy (Sections 3, 4, 8, and 10), which is the binding text.

Encryption & access

Files are encrypted at rest (server-side encryption) and in transit (TLS). Storage keys are namespaced per account, and every file read or delete is gated by an ownership check — one account can never address another account's files, even by guessing IDs.