In-app AI support agent
Support users inside your SaaS dashboard, not just in a chat bubble.
Put an AI support agent inside your product dashboard. Answer onboarding, product, billing, and workflow questions from approved knowledge, then hand off the issues that still need a person.
Why live chat is not enough
The problem is not that users cannot message you. It is that nothing gets done.
Most chat tools are built for conversation volume. SaaS users need an answer while they are still inside the product, not a message receipt and a wait.
The chat bubble answers, then stops
A logged-in user asks how to finish setup, connect an integration, or understand a plan limit. A generic chat tool can collect a message, but it often does not guide the next useful step.
The user still has to wait, search docs, or open another ticket.
Support loses the product context
When every issue becomes a transcript in an inbox, your team still has to infer what the user was trying to do, which screen they were on, and what answer would have unblocked them.
The first human reply starts with discovery instead of resolution.
Marketing and product need different agents
A website visitor needs qualification. A logged-in user needs help inside the workflow. One generic bot usually forces both moments into the same script.
Pre-signup conversion and post-signup support compete for the same behavior.
Two agents, two jobs
Do not force website conversion and product support into the same bot.
A visitor on your pricing page and a logged-in user inside your dashboard are in different moments. MadeAgentic lets you configure each agent around the job that moment needs.
See the SaaS qualification pageBefore signup or demo
Answers pricing, fit, integration, and trust questions, then routes visitors toward demo, trial, docs, or contact capture.
- Pricing-page questions
- Demo qualification
- Trial or sales handoff
After the user logs in
Answers onboarding, product, workflow, billing, and setup questions from approved knowledge while the user is still in the product.
- Onboarding blockers
- Feature and workflow help
- Prepared human escalation
How it works
Put the answer where the user is already stuck.
Not another chat widget. A configured agent that answers from product knowledge, guides the next step, and hands off with context when the answer is not enough.
Create a separate support agent
Keep your public website agent focused on qualification, then configure another agent for logged-in product users.
Ground it in approved product knowledge
Use docs, onboarding notes, FAQs, plan explanations, setup guides, and workflow instructions the agent is allowed to answer from.
Embed it inside the dashboard
Load the agent with the same browser widget pattern inside your authenticated app, product dashboard, onboarding flow, or docs surface.
Route what needs a human
When the question is sensitive, unclear, or outside approved knowledge, the agent captures context and prepares the handoff instead of guessing.
Example conversation
The agent handles the blocker before support gets involved.
The human does not receive a cold complaint. They receive the user question, attempted step, likely blocker, and the answer already given.
Best fit
Strongest when product questions block users from moving forward.
This is strongest when product confusion blocks activation, trial conversion, or support bandwidth. If the question is simple and administrative, a normal helpdesk flow may be enough.
PLG and self-serve SaaS
Help users finish setup, understand features, and get unstuck without waiting for a founder or support rep.
Complex onboarding flows
Explain the next step when users are connecting data, configuring permissions, or learning a workflow.
Lean support teams
Let the agent handle repeat product questions and prepare better handoffs for the issues that still need a person.
Live chat vs agent
Keep the inbox if you need it. Add an agent for the work before the inbox.
The point is not to dunk on support tools. The point is to stop treating every logged-in product question as a message that waits for a human.
FAQ
Questions before you add support inside the app.
Can MadeAgentic run inside an authenticated SaaS dashboard?
Yes. You can embed a MadeAgentic agent inside a logged-in dashboard and use a separate agent from the one on your public website. The dashboard agent should be configured for product support, onboarding, workflow, and handoff questions.
Is this a Crisp, Intercom, or Zendesk replacement?
Not by default. Crisp, Intercom, and Zendesk are broad customer messaging and support platforms. MadeAgentic is useful when you want an objective-driven agent inside the product that answers from approved knowledge and prepares the next step. You can still keep your existing inbox or helpdesk for human escalation.
Does the in-app agent use authenticated account data?
The safe default is approved product knowledge: docs, FAQs, setup instructions, plan explanations, and workflow guidance. Account-aware behavior or live data lookup should be treated as an advanced integration and implemented deliberately.
Can the website agent and dashboard agent behave differently?
Yes. The public website agent can qualify visitors before demo or signup, while the dashboard agent can focus on onboarding, product usage, and support handoff. They should have different objectives, knowledge boundaries, and success criteria.
What should the in-app agent not answer?
Do not let it guess about sensitive account, billing, compliance, security, or contractual issues unless those answers are explicitly approved and supported by the right integration. Route those cases to a human with the conversation context.
How do we know if this is worth adding?
Start with the repeat questions users already ask during onboarding, setup, billing, and feature discovery. If those questions block activation or create repeated support work, an in-app agent is a good test before buying or rebuilding a full support suite.
Build the support agent before users keep opening tickets.
Generate the first agent from your website or product knowledge, tune the objectives, and test the dashboard conversation before users see it.
About 60 seconds