Duckie
Duckie, Inc. · Productivity · Marketing · Leaning
Duckie builds AI support agents from plain English, connects them to your helpdesk and internal tools, then tests each change before it reaches customers.
About Duckie
What Is Duckie
Duckie is an AI customer support platform for companies that sell technical products. You describe what should happen when a ticket arrives, point the agent at your documentation, and connect the systems it needs to read or change. The agent then answers customers and finishes the task instead of handing back a draft.
The difference from a standard helpdesk chatbot is the amount of setup control this AI agent platform gives you. Each agent has a start mode, a knowledge scope, written guidelines, and hard guardrails. Every run leaves a trace you can open, so a support lead can see which article was used, which tool was called, and why a reply was worded a certain way.
The limits matter too. Duckie publishes no price list and no self-serve plan, so you have to book a call before you can judge cost. The builder assumes someone owns your documentation and is willing to write procedures down. Teams that treat support knowledge as tribal memory will get less out of it.
Getting Started
- Create an account at app.useduckie.ai and open the dashboard. You need admin access to at least one support channel.
- Connect a channel under Settings, then Connections. Zendesk, Intercom, and Slack are the common starting points, and authorization runs through OAuth.
- Add knowledge under Train, Knowledge. Link Notion, Confluence, or Google Drive, or paste a website URL and let Duckie index it. Sync usually takes a few minutes.
- Build an agent under Build, Agents. Name it, pick autonomous, workflow, or runbook mode, then attach knowledge, guidelines, and escalation rules.
- Test on real ticket history, review the runs, and only then switch the agent live on customer conversations.
Product Information
A quick look at Duckie's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Support leads at B2B SaaS companies
- Frontline agents on technical products
- Support operations staff
- Small teams without a support engineer
Tasks
- Answering password, billing, and how-to questions from existing documentation, with links and steps pulled from the knowledge base.
- Processing cancellations and refunds
- Triaging incoming tickets by category and urgency so queue order reflects real priority.
- Writing internal notes and Jira tickets when an issue needs engineering, without a support agent retyping the details.
Scenarios
- Ticket volume grows faster than headcount and the queue backs up overnight and on weekends.
- A product launch changes the docs, and you want to replay old tickets to see how the new instructions perform before going live.
- A team is moving from a scripted chatbot to real support ticket automation but wants shadow mode first, with no customer-facing risk.
Key features
Agents Built From Plain English
You write the job in ordinary sentences, and Duckie turns it into an agent with instructions, knowledge access, and tool permissions. Three start modes cover different needs: autonomous agents choose their own steps, workflow agents follow a fixed graph, and runbook agents work through a written procedure. The point is that a support lead can ship a working agent without filing an engineering ticket.
Knowledge Sync and Gap Tracking
Duckie indexes Notion, Confluence, Google Drive, and website content, then lets agents search that material at reply time. Custom articles cover policies that never made it into public docs. Questions the agent could not answer land in a knowledge gaps list, which turns documentation work into a queue you can actually clear.
Runbooks for Repeatable Procedures
A runbook is a numbered procedure in natural language, such as verify identity, check security flags, send a reset link, escalate if verification fails. Agents follow the steps but can adapt when a case does not fit. Snippets let you reuse the same instruction block across several runbooks instead of copying text.
Visual Workflows With Real Branching
For processes that must run the same way every time, the workflow editor gives you action nodes, decision nodes, and AI nodes on a canvas. You can branch on order value, plan type, or account status, then call a tool in each branch. This suits compliance-heavy steps where an autonomous agent has too much latitude.
Tools, Custom APIs, and MCP Servers
Agents can act through built-in tools, connected app tools, custom HTTP tools, and MCP servers. That covers the usual stack: look up a subscription in Stripe, update a Zendesk field, post in Slack, open a Jira issue. Because custom tools are defined by you, internal admin endpoints are in reach too, subject to the permissions you grant.
Guardrails and a Policy Engine
Restrictions run before anything else, then escalation rules check the conversation context. Policies can redact personal data before a tool call, require human approval above a refund threshold, and block messages to external domains. A kill switch stops any agent immediately, and permission scoping limits what each one can touch.
Test and Simulation Before Launch
Duckie can replay past tickets against a draft agent and report how often the output matches what your team actually sent. Shadow mode runs the agent on live traffic without sending replies, so you can compare its answers with human ones. Nothing reaches a customer until you decide the numbers look right.
Pros and cons
Pros
- The agent builder stays in plain language, so support leads can change behavior without waiting on engineers.
- Tool coverage is broad: native integrations, custom HTTP tools, and MCP servers all sit in the same permission model.
- Replay testing and shadow mode make it possible to measure quality before customers see a single AI reply.
- Every run keeps a readable trace of reasoning, knowledge used, and tool calls, which shortens debugging and audits.
Cons
- No published pricing and no self-serve trial, so budget checks turn into a sales conversation before you can test the product properly.
- Quality depends on documentation. Thin or outdated help content produces weak answers, and the knowledge gaps list only helps if someone works through it.
- The setup surface is large: agents, runbooks, workflows, tools, and guardrails each need decisions, which can overwhelm a team without a designated owner.
Frequently asked questions
Duckie handles customer support conversations end to end. It answers questions from your documentation, takes actions in connected systems such as Stripe or Zendesk, and escalates to a person when a guardrail says it should.
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