Intelligent product discovery assistant

Version 1.0 · Last updated 2026-08-10

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 Procedure for discovery. Inside the Procedure, define variables to capture what you would normally ask on a discovery call: segment, region, use_case, risk_profile, company_size, and urgency. The Procedure’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 Procedure to answer only from them, so it does not invent plans, features, or legal statements.

Once variables are set, the Procedure can apply product-fit logic. You can configure this either directly in the Procedure (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 Procedure 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 Procedure should switch to escalation mode. In this mode, it captures additional notes into a variable like case_summary and creates an internal Message 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 Audiences. You can build Audiences such as “SME, EU, onboarding-focused” or “enterprise, multi-country, high-risk” and use them to show 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 Procedures with clearly defined variables, grounding answers in flat Topics, and routing complex cases to humans through Messages and Notifications, it improves fit and provides useful data for later segmentation and in-app messaging.