How can you incorporate DeepResearch results into your work (for example, exporting or sharing the generated report)?

To incorporate DeepResearch results into your work, you can use built-in export features or APIs to integrate reports directly into your existing tools and workflows. Most research platforms support exporting data in formats like PDF, CSV, or JSON, which developers can process programmatically. For example, a CSV export of analysis results could be ingested into a Python script for further statistical modeling or visualization. APIs are particularly useful for automation: if DeepResearch provides a REST endpoint, you could fetch report data on a schedule (e.g., daily) and pipe it into a dashboard or database. This avoids manual downloads and ensures your systems stay updated with the latest findings.

Sharing reports often involves cloud storage integrations, email notifications, or collaboration platforms. For instance, after generating a report, you might configure DeepResearch to save it to a shared Google Drive folder using an API connector, making it instantly available to your team. Alternatively, you could build a Slack bot that posts a summary of the report to a channel when a new analysis completes. For controlled access, some teams use signed URLs or authentication tokens to securely share reports via a private web portal. These methods maintain data governance while streamlining collaboration, especially in distributed teams.

Developers can also extend functionality by customizing exports or building wrappers around DeepResearch outputs. Suppose your project requires specific data transformations—like filtering sensitive information or reformatting timestamps. In that case, you could write a script that processes the exported JSON, applies the changes, and outputs a cleaned version. If reports need to trigger downstream actions (e.g., updating a Jira ticket), webhooks can link DeepResearch to other tools. For example, a completed report on a software bug could automatically generate a ticket in your issue tracker with relevant metrics attached. These approaches turn static reports into dynamic components of your workflow, reducing manual overhead.