Selfint already supports AI and it can be used in several ways.

AI in Selfint is not just a future idea. There are concrete components to help design workflows, run AI steps inside a process and track cost, latency and output.

AI Builder

Suggests next steps and can compose an initial workflow plan from a written objective.

AI inside workflows

AI can read input, interpret content, generate structured JSON and map the result into the next workflow steps.

Controlled operation

Teams can manage AI connections, version prompts, run preview tests and track logs, token usage, cost and latency.

Simple examples

Extract key fields from emails, free text or complex payloads.
Classify requests by type, priority or intent.
Summarise content before sending it to another team or system.
Generate structured JSON so the workflow can continue automatically.
Route to human review when confidence is too low.

What already exists in the AI editor

Provider and connection selection.
Model, system prompt, user prompt and variables.
Input and output mapping.
Preview before saving.
Guard rails such as minimum confidence and fallback actions.
Privacy options controlling what is stored.