Zapier AI

Build AI-powered workflows that connect 7,000+ apps without writing code.

4.5 (4)
Daniel Nikulshynİnceleyen Daniel Nikulshyn·Güncellendi Mayıs 2026

Genel Bakış

Zapier AI extends Zapier's long-running automation platform with generative AI features that help users describe, build, and refine workflows in plain English. It can suggest multi-step Zaps, draft logic, and embed large language model actions directly into automations across thousands of supported applications. Beyond classic triggers and actions, Zapier AI introduces agents and chatbots that can reason over data, call connected tools, and take action on a user's behalf. This makes it useful for teams that want to automate routine work like lead routing, content generation, ticket triage, or internal reporting without committing to custom development.

Temel özellikler

  • Conversational Zap builder
  • AI actions for OpenAI, Anthropic and others
  • Custom AI agents with tool access
  • Built-in chatbot creation
  • Multi-step workflows with branching
  • Tables and Interfaces for data and UI

Kullanım senaryoları

Automate Lead Routing Across Tools

Use natural language to build multi-step Zaps that capture leads, enrich data with AI, and route them to the right CRM, Slack channel, or sales rep.

AI-Powered Ticket Triage

Deploy an AI agent that reads incoming support tickets, classifies urgency, drafts responses with an LLM, and assigns them to the correct queue.

Generate Content in Workflows

Embed OpenAI or Anthropic actions into Zaps to draft marketing copy, social posts, or email replies as part of an automated publishing pipeline.

Internal Reporting Chatbot

Build a no-code chatbot with tool access that pulls data from connected apps and Zapier Tables to answer team questions and summarize reports on demand.

Artılar ve eksiler

Artılar

  • Natural language Zap creation
  • Massive app ecosystem (7,000+)
  • No-code AI agents and chatbots
  • Integrates with major LLM providers
  • Reusable templates and shared workflows

Eksiler

  • Task-based pricing can scale quickly
  • Advanced logic still requires learning curve
  • AI suggestions may need manual cleanup
  • Complex agents can be hard to debug

İncelemeler

4.5

4 puandan ortalama.

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İnceleme bırakmak için giriş yap.

F

Frank Müller

Does the job

Pretty happy overall. Tables and Interfaces for data and UI just works and reusable templates and shared workflows. Complex agents can be hard to debug can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

M

Marcus Bell

Years in this space

I've evaluated a lot of these over the years. What stands out here is multi-step workflows with branching — handled better than most — and integrates with major LLM providers. Worth the time if this is your use case.

C

Camille Laurent

Does the job

Pretty happy overall. Built-in chatbot creation just works and reusable templates and shared workflows. AI suggestions may need manual cleanup can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

B

Beatriz Costa

Solid for our team

We rolled this out across the team last quarter and natural language Zap creation. Tables and Interfaces for data and UI fits neatly into how we already work, and tables and Interfaces for data and UI removed a step we used to do by hand. but it has held up under daily use.

Sorular

Henüz soru yok — ilk soruyu sen sor.

Soru sor

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