DevCite
Privacy

Privacy Policy

How DevCite handles project context, workspace data, AI processing boundaries, and enterprise privacy controls.

Last updated: March 16, 2026

Collection

Project context with explicit boundaries

The platform may use repos, tickets, docs, notes, and related project context, but that context is intended to stay bound to the workspace using it.

Public-site operating model

Processing

AI processing with product-only intent

Workspace data is handled for product functionality and not to train public AI systems.

Public-site operating model

Policy

Structured for leadership visibility

As the platform expands into agents and execution workflows, project boundaries and review checkpoints remain part of the privacy story.

Privacy summary

  • Project-scoped context handling.
  • Zero public-model training on customer data.
  • Protected storage and isolated processing.

Security contact

[email protected]

Use this address for privacy reviews, security questionnaires, and enterprise diligence requests.

1. Our Commitment to Privacy

At DevCite, we understand that your project context can include highly sensitive engineering, product, and operational information. We are building the platform around workspace boundaries, controlled context handling, and privacy-first processing suitable for serious software teams.

2. Zero AI Training Policy

We have strict contracts with our Large Language Model providers. Customer data is not used to train public AI models. Where model providers are involved, processing is intended for product functionality only and not for public training or unrelated reuse.

3. Data Collection and Encryption

DevCite may require access to project systems such as GitHub, Bitbucket, Jira, Linear, Slack, documentation, and other context sources in order to power workspace features. We only collect the data needed to support the product capabilities being enabled and keep stored data protected with strong security controls.

  • Pull request, issue, and repo context used for reviews, analysis, and workspace intelligence.
  • Documentation, notes, and requirement material when a workspace explicitly uses them for grounded output.
  • Project-scoped team and workflow context needed for chat, analysis, and agent coordination.

4. Data Retention

Retention depends on the feature set being used and the operational requirements of the workspace. Our intent is to minimize stored data, isolate project context, and avoid keeping raw synced material longer than needed to support product functionality, auditability, or agreed enterprise requirements.

5. Contact Us

If you have any questions or concerns regarding our privacy practices, please contact our security team at [email protected].