Customers rarely come with perfect keywords. They come with a goal: I need the right product for my use case. Classic shop search then delivers a results list – and leaves the decision to the buyer. This leads to drop-offs, incorrect purchases, returns and support requests.
AGINITY AI AgenticSearch replaces this hit-list logic with genuine product advisory: we understand the use case, derive the right parameters from it and match the entire product catalogue against the requirements. The result is not a top-3 guess, but a complete, traceable selection: all products that truly fit – prioritised and explained.
Results from projects (up to):
What does AI product advisory mean in concrete terms?
AgenticSearch is an agent that handles product consulting in your shop — comparable to a very good specialist advisor in store, but digital and scalable.
Here's how it works:
Understand the use case
What is the product used for? What truly matters?
Capture requirements
Must-haves + preferences + exclusions
Match catalog-wide
The entire catalog is checked against these criteria
Explain & confirm
- Why does the product fit?
- What alternatives are there?
- What should you watch out for? (typical mistakes, compatibility, setup, limitations)
This reduces uncertainty – and makes purchase completion more likely.
How AgenticSearch Works Internally
Not a single chatbot – a team of specialized agents.
When a customer makes a request, several specialized AI agents work together at AGINITY AI. The Communication Agent conducts the conversation and asks follow-up questions. The Search Agent translates requirements into structured database queries and searches the complete catalogue. The Consulting Agent pulls technical details from your datasheets. The Compatibility Agent checks whether accessories and system components fit together. The Price Filter Agent considers the budget. The Expansion Agent suggests useful additions. And the Sales Agent helps with the purchase.
This multi-agent system can be expanded per customer. A tool shop gets different agent configurations than an electronics retailer. The consulting adapts – because each agent is specialized in its field of expertise.
Search Agent
Searches your entire product database in a structured way – not just the top 10, but every single item that meets the requirements.
Consulting Agent
Knows your technical datasheets. Answers technical questions about dimensions, materials, standards and specifications – from real product data.
Price Filter Agent
Considers budget and value for money. Intelligently filters by price ranges without the customer having to move sliders themselves.
Compatibility Agent
Checks what fits together. Finds matching accessories, spare parts and system components – particularly valuable for technical product ranges.
Sales Agent
Clears last-minute purchase doubts. Shows availability, delivery times and provides the final push to decision.
Margin Agent
Weights recommendations by profit margin. Prefers high-margin products – without sacrificing customer satisfaction.
Communication Agent
Conducts the conversation with the customer. Asks targeted follow-up questions and explains recommendations in an understandable way – like an experienced salesperson.
Our key difference from Algolia, Bloomreach, Doofinder, FACT-Finder & Co.
Many search-and-discovery solutions like Doofinder and FACT-Finder excel at improving result lists (ranking, autocomplete, rules). Hybrid AI systems like Algolia and Bloomreach use keyword and vector matching to deliver better search results. AgenticSearch works differently: we shift the work from searching to selecting – with structured database queries instead of vectors, with 7 specialized agents instead of a search index.
Classic:
Keyword → results list → customer compares on their own
AGINITY AI AgenticSearch:
Use case → criteria → all matching products → reasoning + notes → decision
The decisive point: We don't just show a few results, but all products that meet the requirements – transparently and traceably. The agent sets the parameters that correctly search the catalog, instead of leaving the buyer to guess.
The technical difference is fundamental: while RAG-based systems like Qualimero and Frontnow convert your product texts into vectors and deliver a handful of 'probably matching' results via similarity search, AgenticSearch fully queries your structured product database. No top-k, no probabilities – all products that meet the criteria. That's why zero hallucinations: what isn't in the database isn't recommended. Unlike click-based systems like Zoovu, you also need no predefined question paths – your customers can express their requirements in natural language.
Keyword Search vs. RAG vs. Agentic Search
A quick comparison of approaches for product search and consultation in online shops.
| Comparison Criterion | Keyword Search | RAG | Agentic Search |
|---|---|---|---|
| Data Basis | Static fields, keywords, filters | Top-k retrieval from text/vector index (subset) | Complete catalog match via structured product data points per SKU |
| Query Logic | Lexical matching (exact terms) | Similarity search | Attribute-based intent matching |
| Intent Understanding | Low for complex long-tail queries | Medium (depends on embeddings and prompting) | High (use case + attributes + follow-up questions) |
| Hallucination Risk | Low (minimal generation), but relevance gaps | Elevated (generative output) | Significantly minimized (structured product data) |
| Explainability | Medium (filters and scores visible) | Low to medium | High / attribute-based and traceable |
| Update Effort | Medium (synonyms/rules maintained manually) | High (re-indexing) | Low (live catalog) |
Why this delivers measurable results immediately
More Conversion
When buyers no longer have to research and compare at length, conversion rates rise significantly – especially for complex products.
Fewer wrong purchases and returns
Catalog-wide matching plus clear watch-out notes reduces purchasing mistakes.
Fewer Support Requests
When the consultation answers typical product questions during the buying process, follow-up inquiries and cart abandonments decrease.
Typical results (up to):
Which product ranges AgenticSearch is best suited for
AgenticSearch excels when products require explanation, variants/compatibility matter, or customers purchase without domain expertise.
B2C and B2B work equally well – the consultation adapts to the use case.
Integrating expertise: advice becomes industry-specific
AgenticSearch can be enriched with your domain expertise so the consultation works exactly as it makes sense in your industry. For example:
This creates an agent that doesn't just help somehow, but replicates your consultation standard in the shop.
Live in 7 days – via API, EU-hosted
Setup in practice (typical):
Integrations
AgenticSearch integrates with common shop setups, including:
Shopify
Shopware
Magento
WordPressFAQ
What is AI Product Discovery?
AI Product Discovery describes the process by which an AI system infers the buyer's full use case from a search query and, based on that, finds products across the entire catalog — including products the buyer did not explicitly search for. Unlike traditional search, this also reveals latent needs and returns them as explainable product recommendations.
How does Conversational Commerce differ from traditional e-commerce?
In traditional e-commerce, users navigate through categories, filters, and keywords. Conversational Commerce shifts the purchase journey into a natural-language dialogue: the shop understands intent, asks follow-up questions, and guides the decision like a salesperson. This makes product discovery much more precise and shortens the path to purchase.
What is the difference between an Agentic Shopping Assistant and product search?
Traditional product search returns result lists based on keywords and filters. An Agentic Shopping Assistant understands the purchase intent behind the query, analyzes the full product catalog, asks follow-up questions when needed, and explains why a product is recommended. It does not just replace search — it provides digital product advice.
What is AI product consulting in an online shop?
A consultation that understands the use case, captures requirements, and derives a complete product selection from the catalog — including reasoning and notes.
Why is this better than normal search?
Because buyers rarely have perfect keywords. AgenticSearch replaces keyword guessing with criteria matching and helps with the decision, not just the finding.
Does AgenticSearch really show all matching products?
Yes. We match catalog-wide and can show all products that meet the requirements — prioritized and explained.
How quickly can you get started?
In many cases within 7 days, depending on catalog data and integration.
Try now
Try now: Pilot AgenticSearch with your catalog.
Get in touch: Send a use case – we'll show you the right integration.
