Type it, done.
Create any automation by describing it in plain language. No flowcharts, no drag-and-drop wiring, no developer required.
Connect Chatbotkit and Gleap — AI-native workflow automation
Build multi-step AI automations that flow through Chatbotkit and Gleap — and chain in 1000+ other apps when the workflow needs them. One instruction, many steps, every API call run by AI. No code, no flowcharts, self-healing.
Chatbotkit and Gleap are two anchors of a much bigger automation. Definable Workflow chains your AI layer, your support desk, and as many other tools as the job needs into a single multi-step flow — built, executed, and verified by AI. Describe the outcome; Workflow plans the steps, runs every API call, branches on conditions, fans out in parallel where useful, retries on failure, and self-heals when an upstream API shifts shape.
What you can automate between Chatbotkit and Gleap
6 ways to automate Chatbotkit + Gleap
- When a model output is generated in Chatbotkit, route the ticket in Gleap
- When a tool call completes in Chatbotkit, post a draft reply in Gleap
- When a new ticket is opened in Gleap, route the output to the right system in Chatbotkit
- When a ticket is escalated in Gleap, log the generation in Chatbotkit
- When a generation is rated in Chatbotkit, log the resolution in Gleap, and log the result in a structured record for team review
- When an evaluation finishes in Chatbotkit, notify the right specialist in Gleap, then send a notification to the assigned owner with the full context
Most automation tools run code. Workflow runs on AI.
AI builds it AND runs it.
Other platforms use AI to generate brittle code that runs offline. Workflow's entire execution layer is AI — it interprets, decides, and acts step by step.
Self-healing by design.
Every step is monitored by a verification layer. If something fails — a rate limit, an unexpected response, a skipped condition — Workflow detects it, corrects it, and reruns automatically.
Cross-tool orchestration.
Multi-step, event-driven, condition-based flows across Chatbotkit, Gleap, and 1000+ other tools — all from one instruction.
How Definable Workflow automation works — step by step
- 1
Describe your workflow
Type what you want to happen between Chatbotkit and Gleap. No setup wizard.
- 2
Workflow builds it automatically
Definable maps your instruction to the right actions across both tools. You see the plan before it runs.
- 3
AI executes every step
Workflow runs end-to-end. It calls Chatbotkit, processes the data, applies your conditions, and fires the Gleap action.
- 4
The verification layer monitors everything
Every step is verified. If something fails, Workflow catches it, adjusts, and retries — without you lifting a finger.
Example workflow
Trigger
When attach dataset file in Chatbotkit
Condition
If the Chatbotkit event matches your configured filter
Action
Archive All Tickets in Gleap
Verified
Workflow verified all steps completed successfully.
Frequently asked questions
What can I automate between Chatbotkit and Gleap?
Anything you can describe. Workflow connects Chatbotkit (your AI layer) and Gleap (your support desk) through a single instruction. Examples: when a model output is generated in Chatbotkit, route the ticket in Gleap; when a tool call completes in Chatbotkit, post a draft reply in Gleap. There's no limit to step count or branching depth.
Do I need to write code to connect Chatbotkit and Gleap?
No. You describe the outcome in plain language and Workflow assembles the steps, authenticates both tools, runs the flow end-to-end, and self-corrects if anything fails.
What happens if Chatbotkit or Gleap returns an unexpected response?
Workflow has a verification layer on every step. If a response is malformed, an API rate-limit hits, or a condition is unmet, Workflow detects it, adjusts, and reruns the step automatically — without breaking the flow.
How is this different from drag-and-drop automation tools?
Most automation tools generate static workflows that run brittle code. Workflow is AI-native: the execution layer interprets each step at runtime, so it adapts when Chatbotkit ships an API change or Gleap returns a new field shape. You don't maintain it.
How long does it take to set up?
Minutes. Authenticate Chatbotkit and Gleap, type what you want to happen, review the plan Workflow generates, and start running.
Your workflows. Built by AI. Run by AI. Fixed by AI.
Stop maintaining automations. Start describing outcomes. Workflow handles everything between Chatbotkit, Gleap, and your entire stack.