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AI Workflow Automation

Build AI workflows around prompts, not plumbing

Drag-and-drop canvas for prompt chains, branching logic, and model selection. Deploy to chat, API, or schedule. No code, no infrastructure.

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Workflow Builder
Aisle workflow builder: visual AI automation canvas with prompt node configuration

Works with every model

  • Anthropic
  • OpenAI
  • Gemini
  • xAI
  • OpenRouter
  • Amazon
  • Perplexity
  • MoonshotAI
  • Meta
  • Qwen
  • DeepSeek

AI workflows built around prompts and reasoning

Most automation tools bolt AI onto generic data pipelines. Aisle starts with the prompt.

Version-controlled prompts

Change a prompt once and every workflow using it updates. No hunting through node configs.

Any model per node

Use Claude for analysis, GPT for drafting, Gemini for summarization. Switch models with a dropdown.

Branching, loops, and logic

Route to different prompt chains based on classification. Retry failed steps. Handle exceptions visually.

Persistent memory

Workflows remember past results. A monitoring workflow deduplicates. A research pipeline picks up where it left off.

Full execution logs

See exactly which prompt version ran, what inputs were sent, and what the model returned. Every time.

Team sharing and permissions

Editors refine prompts. Operators run workflows. Admins control model access. Everyone has the right level of access.

See how it works

Prompt chains on a visual canvas

Drag prompts onto a canvas and connect them. Each node runs a versioned prompt with its own model, variables, and output schema. Branch based on AI classification. Loop until a condition is met. No code, no YAML, no JSON configs.

See it in action

Pick any model per node

Use Claude for nuanced analysis, GPT for structured extraction, Gemini for summarization — all in the same workflow. Switch models with a dropdown. Your prompts work across any provider without changes.

See it in action

Deploy to Slack, JIRA, API, or schedule

The same workflow runs from a Slack command, a JIRA webhook, an API call, or a cron schedule. One source of truth for the logic. Deploy once, trigger from anywhere.

See it in action

Enterprise controls without enterprise complexity

Governance, audit, and access controls built in from the start.

Scoped access per prompt

Control who can edit prompts, run workflows, and access which models. Role-based permissions from day one, not an enterprise add-on.

Execution logs on every run

Full trace of every workflow execution: which prompt version ran, what inputs were sent, what the model returned, and how long it took. Exportable for compliance.

Admin model controls

Set which models are available to your organization. Control costs by restricting expensive models to specific teams or use cases. Track spend per team, per workflow.

Five reasons to switch from ad-hoc to AI workflows

1Run AI workflows on a schedule — daily reports, weekly analyses, hourly monitoring
2Turn any prompt chain into a repeatable, auditable process
3Deploy the same workflow to chat, API, or webhook with one click
4Persistent memory so workflows pick up where they left off
5One platform instead of Zapier + LangSmith + custom glue code
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Works with the tools you already use

SlackGitHubJiraGoogle DriveGmailOutlook MailPostgreSQLAWS BedrockAzure SQLAsanaAirtableGongPipedriveAffinitySlackGitHubJiraGoogle DriveGmailOutlook MailPostgreSQLAWS BedrockAzure SQLAsanaAirtableGongPipedriveAffinity
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AI workflows built for reasoning, not just routing.

Prompt-first automation with model selection, version control, and enterprise governance.

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