This is a submission for the Algolia Agent Studio Challenge: Consumer-Facing Non-Conversational Experiences
What I Built
Silent Plumbing Assistant is a non-conversational, visual-first AI agent designed for small retail environments such as hardware and plumbing stores.
In many real-world retail situations, customers cannot describe what they need because they don’t know English or the technical name of a product. They often rely on gestures, partial words, or showing broken parts. Traditional solutions like search bars or chatbots fail because they require language.
This agent works silently and proactively. When a retailer opens a category like Plumbing, the agent automatically retrieves and narrows the most relevant products based on context such as material type (e.g., CPVC), common sizes (¾”, 1”), and typical retail demand. Large, clear product images are displayed so customers can simply point to the correct item.
No typing.
No chat.
No English required.
Demo
Demo / Prototype link:
( add your link here – GitHub, Figma, or simple hosted page )
Example demo flow:
- Retailer opens the Plumbing category
- The agent auto-retrieves valves and fittings
- Retailer selects CPVC
- The agent prioritizes common sizes like ¾” and 1”
- Visual results appear instantly
- Customer points to the needed item and completes the purchase
Even a basic mockup or static demo illustrates the core intelligence clearly.
How I Used Algolia Agent Studio
Algolia Agent Studio is used as the orchestration layer that decides when and what information should appear, without requiring explicit user queries.
Product data such as:
- Category (plumbing, valves, fittings)
- Material (CPVC, PVC, brass)
- Size (½”, ¾”, 1”)
- Product type (ball valve, handle, fitting)
is indexed in Algolia.
When contextual signals occur (for example, a category is opened or a material is selected), the agent triggers retrieval automatically. Algolia’s faceted search and ranking capabilities are used to narrow and prioritize results based on relevance and common demand, transforming a static catalogue into a proactive assistant.
Why Fast Retrieval Matters
This experience depends on instant response. In a retail environment, even small delays break the flow between the retailer and the customer.
Algolia’s fast, contextual retrieval ensures that:
- Results appear immediately when context changes
- Product narrowing feels natural and effortless
- The agent enhances the workflow instead of interrupting it
Because retrieval is fast and precise, the agent feels invisible yet helpful — which is essential for a non-conversational experience.
Thanks for reviewing my submission!
Top comments (1)
This project is inspired by real-world retail challenges where language becomes a barrier. Happy to hear feedback.
That’s it. No spam.