The Jet Admin DeepSeek integration lets your app send prompts and reasoning tasks to DeepSeek and read the result back into your own records, driven by the same tables, forms and record actions you already build in Jet. Whether a ticket needs summarizing, a decision needs working through step by step, or a large batch of records needs classifying, the whole workflow runs inside Jet, and every call is scoped by role and written to the audit log.

That covers what teams build on DeepSeek first: cost-efficient batch processing for high-volume classification, support consoles that draft replies from a ticket thread, and workflows that need a model to reason through a multi-step decision rather than just pattern-match. Because Jet reads your own database alongside DeepSeek, every prompt can pull in the exact record it's working on, and every response writes straight back to it.

Key benefits

• AI without a separate app. Generate text, summaries and classifications from inside the same screens your team already works in.
• Prompts built from your own data. Pull fields from a record straight into a prompt, instead of copying and pasting between tools.
• Results written back automatically. Store a completion, summary or extracted field directly on the record it came from.
• Actions with a record. Every DeepSeek call runs inside your app, scoped by role, and written to the audit log.
• Complete workflows. Tie a generated result to everything that follows it: update a record, trigger a notification, route for review.
• Cost-efficient at scale. Run classification and summarization across large batches of records without the cost of a larger model.

How to connect Jet Admin with DeepSeek

1. In DeepSeek, open the platform console and create an API key.
2. In Jet Admin, open your project and add a new resource, or pick DeepSeek while creating a project.
3. Choose DeepSeek and paste the API key.
4. Jet loads the DeepSeek actions available: completions and reasoning tasks.
5. Add the data sources you want to feed into prompts.
6. Generate the screens you need, then set role permissions on who can trigger AI actions.
7. Add actions and workflows on top: batch classification jobs, ticket summaries, and a review step before AI output is saved.