Use AI to recommend products
Automate the repeat path and keep human handoff clear.
3-day free trial · No charge during trial
What it handles
Works with
Why it matters
The practical reason to use it.
Manually handling product recommendations inside your product is slow, inconsistent, and hard to scale.
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 preferences, use case fit, and.
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 in-product conversations 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.
Task flow
How the assistant handles repeat work.
Product Recommendations
The agent recommends products inside your product by collecting preferences, use case fit, and catalog context before it decides what should happen.
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.
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.
Accuracy controls
How answers stay accurate.
Grounded in your sources
Responses stay tied to the docs, policies, and structured data your team already trusts for product recommendations.
Rules before replies
Use approval logic, routing thresholds, and business rules before the workflow changes status or triggers downstream actions.
Human review when needed
InsertChat hands off the edge cases, exceptions, and judgment calls instead of pretending every conversation should be fully automated.
Visible automation performance
Track which conversations resolved end-to-end, where escalation happened, and what to tighten next for better throughput.
Add next
Useful next automations.
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.
Protect checkout momentum
Handle shipping, payment, and cart questions right where the conversion decision happens.
Automate post-purchase updates
Keep tracking, returns, and order changes in the same conversational workflow instead of bouncing customers across pages.
Increase basket size cleanly
Recommend add-ons, bundles, and complementary products based on what the shopper is already considering.
What you get
The changes teams should notice first.
- 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
Try the FAQ like a visitor.
Open product, pricing, security, integration, and free-tool questions in the same chat your visitors use.
InsertChat
Interactive FAQ
Hey. Pick a question below and see how InsertChat turns FAQs into clear, source-backed answers.
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 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 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 inside your product?
Yes. The agent recommends products 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 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.
Ready to get started?
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3-day free trial · No charge during trial