Frequently Asked Questions
An AI shopping agent helps customers find and evaluate products through conversation. Depending on its integration level, it may search catalogs, recommend products, compare options, check variants or availability, answer product questions, and perform approved actions such as preparing a shopping cart.
There is no standard price because the scope varies significantly. A catalog-based recommendation assistant is much simpler than an agent connected to inventory, customer accounts, carts, ERP systems, and multiple ecommerce channels. Development cost should be estimated after defining the agent's data sources, actions, integrations, and security requirements.
Yes. An agent can be added to an existing ecommerce architecture when the required product and commerce information can be accessed safely. Integration may use ecommerce platform APIs, search services, middleware, product databases, PIM systems, or custom backend services.
Not necessarily. Conversational discovery and traditional search can work together. Search remains useful when customers know exactly what they want, while an AI assistant can be more useful for exploratory queries such as finding a product based on needs, preferences, budget, compatibility, or intended use.
Yes, provided the agent is connected to a reliable inventory source. The AI should retrieve current availability before recommending stock-sensitive products rather than depending on old product information stored in its conversational context.
They can when the ecommerce platform exposes suitable cart functionality and the agent has permission to use it. The application should validate products and variants before changing the cart and clearly control which actions the AI can perform.
Evaluate both AI and ecommerce engineering experience. Ask how the company handles product retrieval, hallucinations, live inventory, APIs, permissions, customer data, analytics, cart actions, failure handling, and integration with your current ecommerce platform.
No. Smaller brands can use conversational product discovery when their catalog or buying process benefits from guidance. Custom development becomes more relevant as catalogs, integrations, customer journeys, or transactional requirements become more complex.