Use AI to route approval requests
Use AI to handle this task faster and pass the hard cases to a person.
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What it handles
Works with
Why it helps
See why it helps in real life.
Manually handling approval routing inside your product is slow, inconsistent, and hard to scale. Operations and compliance teams get slowed down by missing paperwork, manual follow-ups, and inconsistent explanations of approval rules. The real cost is not only the time spent on the reply itself, but the context the team has to rebuild before the request can move forward.
InsertChat automates route approval requests inside your product without improvising outside the rules your team already uses by combining your knowledge base, business rules, and escalation paths into a single agent. The agent routes approval requests, follows your approval logic, and hands off edge cases to a human with full conversation context.
Once the agent is live across in-product conversations, it handles approval routing end-to-end by collecting approval level, policy thresholds, and ownership, taking the next approved action via send the case to the right approver with the right context, and escalating anything outside its scope. Teams typically see faster resolution, fewer dropped conversations, and clearer visibility into what gets automated versus what still needs a person.
How it works
A step-by-step look at the workflow.
Step 1
A visitor starts a conversation inside your product — the agent identifies the intent and begins collecting approval level, policy thresholds, and ownership before it tries to move the request forward.
Step 2
The agent checks your knowledge base and Document storage, Approval flows, Policy rules to determine the right next step.
Step 3
Once enough context is gathered, the agent routes approval requests while following your policies and approval logic.
Step 4
If the request falls outside the agent's scope, InsertChat escalates to a human via in-product conversations with the full conversation summary attached.
Step 5
You review which approval routing conversations resolved end-to-end, where escalation happened, and what rules to tighten for better throughput on the next rollout.
How it handles the task
See how the agent handles the work.
Approval Routing
The agent routes approval requests inside your product by collecting approval level, policy thresholds, and ownership before it decides what should happen next. That keeps the workflow tied to real context instead of a generic chatbot reply.
In-app Chat coverage
Deploy the same workflow across in-product conversations next to the workflow the user is trying to complete, so the task starts where users already expect help. It keeps the experience consistent whether the conversation begins on a website, in chat, or inside an internal surface.
Policy-first decisions
Ground responses in approved sources, thresholds, and escalation rules before the agent takes the next step.
System actions and handoff
Once the conversation is ready, InsertChat can send the case to the right approver with the right context, and it can escalate to a human with the summary already attached. That way the next owner starts from the approved action instead of rebuilding the thread from scratch.
Why it stays on track
See how it stays accurate and safe.
Grounded in your sources
Responses stay tied to the docs, policies, and structured data your team already trusts for approval routing. The workflow stays usable in production because the agent answers from approved material instead of improvising.
Rules before replies
Use approval logic, routing thresholds, and business rules before the workflow changes status or triggers downstream actions. That gives the team a visible control layer for exceptions, sensitive cases, and high-value requests.
Human review when needed
InsertChat hands off the edge cases, exceptions, and judgment calls instead of pretending every conversation should be fully automated. The agent keeps the context attached so the human owner can continue without asking the same questions again.
Visible automation performance
Track which conversations resolved end-to-end, where escalation happened, and what to tighten next for better throughput. That makes it easier to expand the workflow once the first deployment proves itself.
What to add next
See what you can automate next.
Collect complete submissions
Use the workflow to request missing files, clarify requirements, and keep the intake experience moving without repeated outreach. That makes it easier to extend approval routing into a wider automation system over time.
Verify before approval
Check identity, document quality, and policy thresholds before the case reaches a human reviewer. That makes it easier to extend approval routing into a wider automation system over time.
Explain rules clearly
Turn dense policy language into next-step guidance that applicants and customers can actually act on. That makes it easier to extend approval routing into a wider automation system over time.
Keep records reviewable
Attach a clean audit trail so reviewers can see what the agent collected, checked, and escalated. That makes it easier to extend approval routing into a wider automation system over time.
What you get
These are the main things you should notice once it is live.
- Less manual work on repetitive conversations
- Faster resolution without human bottlenecks
- Consistent execution every time, at any scale
- Clear visibility into what gets automated and what doesn't
What our users say
Businesses use InsertChat to launch branded assistants faster and keep their knowledge in one branded AI assistant.
Finally, one place for all my AI needs. The ability to switch models mid-conversation is game-changing.
Sarah Chen
Product Designer, Figma
We deployed AI support in 20 minutes. Our response time dropped by 80%. Customers love it.
Marcus Weber
Head of Support, Notion
The white-label option let us offer AI services to our clients overnight. Revenue grew 40% in Q1.
Elena Rodriguez
Agency Founder, Digitale Studio
Commonquestions
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Use AI to route approval requests FAQ
Can an AI agent route approval requests without human approval?
Yes — you configure exactly which approval routing actions the agent takes autonomously and which require human review. For example, the agent can route approval requests while following your policies and approval logic on its own, but escalate edge cases based on thresholds you set. Routine approval routing cases resolve end-to-end while exceptions get flagged for a person to review.
How does the agent know how to route approval requests correctly?
The agent is grounded in your knowledge base and Document storage, Approval flows, Policy rules. It collects approval level, policy thresholds, and ownership before deciding the next step, and it can send the case to the right approver with the right context once enough context is gathered. It never improvises — it follows the sources and logic you configure, then keeps the next owner in the loop when the workflow needs a handoff.
What happens when the agent can't handle a approval routing request?
InsertChat hands the conversation to a human via in-product conversations with the full context already attached — the user doesn't repeat themselves. You configure when handoff triggers based on confidence thresholds, request complexity, or approval level, policy thresholds, and ownership that falls outside the agent's scope. The result is a cleaner escalation instead of a dead-end chat.
Does approval routing automation work inside your product?
Yes. The agent routes approval requests across in-product conversations next to the workflow the user is trying to complete. The same workflow, knowledge base, and escalation rules apply regardless of where the conversation starts, so the task execution stays consistent at any scale and across every channel you enable.
How do teams measure whether approval routing automation is working?
Teams usually measure resolution time, handoff quality, and how many conversations finish without manual re-entry. If those numbers improve, the workflow is doing real work instead of just deflecting messages. That makes it easier to expand the automation into adjacent steps once the first path is reliable.
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