Use AI to recommend products
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 product recommendations in your customer portal is slow, inconsistent, and hard to scale. Commerce teams lose revenue when product questions, shipping uncertainty, and returns friction force shoppers to wait for a human answer. 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 recommend products in your customer portal without splitting the experience by language or geography by combining your knowledge base, business rules, and escalation paths into a single agent. The agent recommends products, follows your approval logic, and hands off edge cases to a human with full conversation context.
Once the agent is live across authenticated customer sessions, it handles product recommendations end-to-end by collecting preferences, use case fit, and catalog context, taking the next approved action via surface the best product path without making the shopper browse blindly, 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 in your customer portal — the agent identifies the intent and begins collecting preferences, use case fit, and catalog context before it tries to move the request forward.
Step 2
The agent checks your knowledge base and Catalog data, Order systems, Checkout events to determine the right next step.
Step 3
Once enough context is gathered, the agent recommends products for multilingual audiences and global teams.
Step 4
If the request falls outside the agent's scope, InsertChat escalates to a human via authenticated customer sessions with the full conversation summary attached.
Step 5
You review which product recommendations 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.
Product Recommendations
The agent recommends products in your customer portal by collecting preferences, use case fit, and catalog context before it decides what should happen next. That keeps the workflow tied to real context instead of a generic chatbot reply.
Customer Portal coverage
Deploy the same workflow across authenticated customer sessions when the workflow depends on account data and prior activity, 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.
Multilingual execution
Use one workflow across regions while keeping the same rules, escalation points, and knowledge sources in place.
System actions and handoff
Once the conversation is ready, InsertChat can surface the best product path without making the shopper browse blindly, 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 product recommendations. 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.
Guide shoppers to the right product
Use the same agent to compare options, surface fit guidance, and answer objections before the shopper leaves the session. That makes it easier to extend product recommendations into a wider automation system over time.
Protect checkout momentum
Handle shipping, payment, and cart questions right where the conversion decision happens. That makes it easier to extend product recommendations into a wider automation system over time.
Automate post-purchase updates
Keep tracking, returns, and order changes in the same conversational workflow instead of bouncing customers across pages. That makes it easier to extend product recommendations into a wider automation system over time.
Increase basket size cleanly
Recommend add-ons, bundles, and complementary products based on what the shopper is already considering. That makes it easier to extend product recommendations 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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InsertChat
Product FAQ
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Use AI to recommend products FAQ
Can an AI agent recommend products without human approval?
Yes — you configure exactly which product recommendations actions the agent takes autonomously and which require human review. For example, the agent can recommend products for multilingual audiences and global teams on its own, but escalate edge cases based on thresholds you set. Routine product recommendations cases resolve end-to-end while exceptions get flagged for a person to review.
How does the agent know how to recommend products correctly?
The agent is grounded in your knowledge base and Catalog data, Order systems, Checkout events. It collects preferences, use case fit, and catalog context before deciding the next step, and it can surface the best product path without making the shopper browse blindly 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 product recommendations request?
InsertChat hands the conversation to a human via authenticated customer sessions with the full context already attached — the user doesn't repeat themselves. You configure when handoff triggers based on confidence thresholds, request complexity, or preferences, use case fit, and catalog context that falls outside the agent's scope. The result is a cleaner escalation instead of a dead-end chat.
Does product recommendations automation work in your customer portal?
Yes. The agent recommends products across authenticated customer sessions when the workflow depends on account data and prior activity. 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 product recommendations 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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