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AI in Signailist

Overview​

AI is used in four primary workflows:

  1. Relevance scoring of news against workspace themes.
  2. Text generation for digests and publication drafts.
  3. Template generation from Telegram sources through style extraction.
  4. Image generation for digest covers and related visuals.

AI analysis and generation use the selected workspace’s balance. You do not need your own OpenAI API key.

Collection and publication also use the workspace balance. See Balance and usage.

1. Theme-based relevance scoring​

How scoring works​

Each theme is defined as an AI-first interest profile:

  • What to look for: a plain-language description of relevant content.
  • What to exclude: a plain-language description of irrelevant content.

Each incoming news item is evaluated against each active theme with:

  • Relevance: whether the item matches the theme.
  • Score: a value between 0 and 1.
  • Confidence: how sure the model is.
  • Reason: a short explanation of the decision.

Scoring settings​

At workspace level, teams can configure:

  • The AI model used for scoring.
  • Request-per-minute limits.
  • Processing concurrency.

If an item has already been scored for the same theme, repeated scoring is skipped automatically.

2. Digest text generation​

How generation works​

  1. A user selects digest candidates and a template.
  2. AI generates the digest text from the chosen news items.
  3. The user reviews, edits, and approves the result.
  4. In Publication, prepare and approve the saved revision, then select Telegram, Discord or several destinations. Formatting and message limits depend on the service. See Delivery channels.

Materials from supported sources and external intake use the same topic and digest AI workflows. For the first saved feed snapshot, explicitly prepare the entries for AI. See Sources.

See Digests for the full workflow.

3. Template generation from a source​

How template generation works​

You can define a template’s tone and structure directly, without connecting Telegram. Style extraction from a Telegram source is an optional way to create a template. See Digests.

Template generation analyzes an existing Telegram source and creates a reusable writing template from that source's tone and structure.

  1. The user selects a source and a sampling window.
  2. AI analyzes tone, structure, and writing patterns.
  3. A template is produced with style settings.
  4. Optional test generations can be produced to validate the output quality.

Template generation​

Template generation analyzes source posts and creates a style profile with optional test generations for quality validation.

4. Image generation​

How image generation works​

For a digest, Signailist can generate a cover image:

  1. AI builds an image prompt from the digest content and the template settings.
  2. The image is generated using the model selected in the template.

Image settings​

Each digest template can define:

  • Whether image generation is enabled.
  • A base image prompt.
  • Preferred size or aspect.
  • Reference images.

Limitations​

  • AI analysis and generation require enough balance in the selected workspace.
  • Models can be configured at workspace level for scoring and at template level for generation.
  • To generate a cover, enable image generation in the template and select an image model.