Marqo is a platform designed to rapidly prototype, speed up iteration, and seamlessly deploy over 150 embedding models. It aims to build powerful AI applications and transform retrieval stacks, optimizing search conversion using click-stream, purchase, and event data. This creates a personalized experience that anticipates customer needs.
What is Marqo?
How to use
Connect your product catalog and user behavior data to Marqo. The platform trains a custom LLM for your brand, and then you connect to your new AI search engine. No UI changes are needed.
Core Features
- AI-powered search
- Personalized recommendations
- Image and multimodal search
- Customizable search
- Embedding model deployment
Use Cases
- Supercharge onsite product search with AI for e-commerce
- Build chatbots, agents, and assistants that use AI
- Deliver highly personalized content recommendations
FAQ
What does Marqo do?
Marqo optimizes search conversion using click-stream, purchase and event data, creating a personalised experience that knows what your customers are looking for - better than they do.
How does Marqo personalize search results?
Marqo personalizes results by learning from individual customer interactions, adapting to browsing patterns and preferences, making each search tailored to the user.
What kind of search does Marqo support?
Marqo supports search by meaning, eliminating zero-result pages and helping users find what they need, even with highly descriptive or uncommon search terms. Marqo’s semantic search interprets query intent to deliver accurate, relevant results that improve engagement and conversions. It also supports image and multimodal search.
Pricing
Marqo Cloud
Marqo scales seamlessly with your catalogue size, users and use-cases. Easy – just like the rest of Marqo.
Professional Services
Whether you’re just getting started with AI, or looking to refine your existing embeddings stack, we’re here to help you succeed.
Pros & Cons
Pros
- Improved search conversion and relevance
- Personalized search results based on user behavior
- Automated tagging and collection generation
- Support for image and multimodal search
- Seamless integration with existing stacks
Cons
- Requires user interaction data for personalization
- Custom LLM training may take time
- Reliance on AI models can introduce unexpected behavior