This recipe shows how to layer a conversational assistant on top of product and plan pages so visitors can choose with confidence.
Many regulated organizations publish accurate product and pricing information but still see visitors hesitate or choose poorly. Pages are dense, options depend on eligibility rules, and visitors often do not know where to start. A product discovery assistant addresses this by asking targeted questions, applying your rules, and then presenting a small set of suitable choices.
To make this work, configure a dedicated AI Skill for discovery. Inside the skill, define variables to capture what you would normally ask on a discovery call: segment, region, use_case, risk_profile, company_size, and urgency. The skill’s prompt flow should ask only for variables that are still empty, so simple visitors go through fewer steps while complex ones still provide enough detail.
To avoid hallucination, limit the assistant’s knowledge to trusted sources and a flat Topics taxonomy. Example Topics include Products, Pricing-basics, Eligibility, Risk-policies, and Use-cases. Tag relevant content under these single-layer Topics and instruct the skill to answer only from them, so it does not invent plans, features, or legal statements.
Once variables are set, the skill can apply product-fit logic. You can configure this either directly in the skill (conditional branches on variables) or via a small routing function that maps combinations like segment=SME, region=NL, risk_profile=low to a recommended subset of products. The skill then explains each suggestion in simple language and links to the right next step (quote, trial, portal signup).
When the conversation reveals complexity or sensitivity—for example, risk_profile=high or unusual constraints—the skill should switch to escalation mode. In this mode, it captures additional notes into a variable like case_summary and creates an internal task for sales or success, including all variables and the AI-generated summary. You can also trigger an internal notification to a Slack or email channel so the right team sees high-value or tricky leads quickly.
The captured variables are also useful for audience segmentation. You can build segments such as “SME, EU, onboarding-focused” or “enterprise, multi-country, high-risk” and use them to show in-app notifications in your website or portal later on. For example, visitors who chose a “compliance” use case might see an in-app banner pointing to a policy explainer or demo video tailored to that topic.
Conclusion
An intelligent product discovery assistant makes complex product choices easier for visitors while respecting your rules and constraints. By using AI Skills with clearly defined variables, grounding answers in flat Topics, and routing complex cases to humans through tasks and notifications, it improves fit and provides useful data for later segmentation and in-app messaging.
