AI & Machine Learning
Build an AI-powered search and discovery experience
Kliv is an AI app builder. Describe your catalog, search rules, photo lookup, fit checks, and checkout flow, and it builds AI-powered search and discovery around them.
Just enter your idea into the text box and AI will build it for you
What is Kliv?
Kliv isn’t a hosted search box you connect to your site — it’s an AI app builder that builds software for you.
You describe the app you need in plain words, and Kliv builds it: the database, product screens, search logic, file handling, checkout, and admin workflows. What you get is a real web app that’s yours, live on your own domain, and you can keep changing it by asking for changes.
On this page, that app is AI-powered search and discovery for a product catalog. Kliv builds all kinds of web apps; catalog search is one example.
Why build your own search and discovery?
Customers often do not know the exact product name or part number. They describe the problem: a hinge for an old deck locker, a fabric by weight and color, a broken appliance part in a photo.
Kliv builds the catalog around those searches. Search runs over your own product rows, notes, and photos, so a customer’s words can find the item even when no product title uses the same phrase.
The catalog beneath the search box
Good discovery starts with good records. These are the pieces Kliv can build around.
The SKU record
Dimensions, material, thread pitch, and fit notes. Search quality depends on these rows, and you can keep enriching them.
The fitment map
Boat make, year, hull details, and compatible parts. “Fits a 1988 Meridian 32” is stored as data, not buried in a product description.
The photo index
Product images are described and indexed when uploaded, so a customer’s photo can be matched against products on your shelf.
The ask log
Every search lands here, matched or missed. Misses become a merchandising report: what customers asked for in their own words.
The boat profile
A signed-in customer’s boats live on that customer’s record. Results and fit checks can read it, and only that customer can see it.
An example: Lena’s marine hardware store
Here’s how one store might use it. It’s only an example — you would describe your own catalog, customer records, and checkout rules.
A photo replaces the part name
A customer on her boat uploads a photo of a corroded hinge. Image search returns three candidates from Lena’s own catalog, not a generic internet result.
The fit check narrows the result
The customer’s profile says 1988 Meridian 32. The fitment map removes two candidates, flags the listed replacement, and explains why.
Checkout stays in the same app
The $34 hinge moves from search result to paid order without changing systems. Checkout runs on Lena’s own Stripe account, and the order row sits in the same database the search just read.
A miss becomes a stocking decision
Someone searches “teak bung 12mm” and gets no result. The ask log stores it, and the weekly digest turns it into a Monday buying decision.
The assistant answers after hours
A late customer asks whether the hinge takes a #10 fastener. The store assistant answers from the SKU record and installation notes, and the exchange is saved for counter staff to review.
Describe what you want to build
Kliv builds from your description, so the more detail you give, the closer the first version. Describe the catalog fields, photo lookup, customer context, failed-search handling, and checkout. Here are three to build on:
Marine hardware search
Fit-aware search for a parts catalog.
“Build AI-powered search for my marine hardware store. Index 6,000 SKUs with dimensions, material, thread pitch, photos, and boat fitment notes; let customers search in plain words or upload a photo; use each customer’s saved boat profile for fit checks; and sell parts through checkout on my own Stripe account.”
Fabric by description
Search by weight, weave, and color.
“Build a fabric shop where customers search by describing weight, weave, color, stretch, and use case. Results should come from semantic search over my inventory, swatches should be orderable from the result page, and failed searches should be logged for weekly review.”
Parts photo lookup
Match a broken part to its replacement.
“Build a spare-parts finder for my appliance parts store. Customers upload a photo of the broken part, image search suggests matches from my catalog, staff can review unmatched searches, and customers can open a support thread when no result is found.”
Search as a database feature
No second system to feed. Kliv’s semantic search runs over the rows and files you already have. Improve a SKU record, and the next search can use the better data.
Photos are first-class. Uploaded images are described and indexed automatically. That is what makes photo-in, product-out lookup possible, and it works on your product photography.
The schema grows when the catalog needs it. If counter staff need a thread-pitch field, you ask for it. Kliv updates the app so the field exists, and search gets better because the data got better.
Misses are routed, not dropped. A scheduled job can turn failed searches into a weekly email, and a support thread can pick up the customer who asked.
Everything above stands on the same floor: a real database with per-row access rules, typed server-side functions, and files that live beside the rows they describe. The backend page is the tour of that foundation.
Common questions
What exactly is Kliv?
Kliv is an AI app builder that creates custom web apps from a description. For search and discovery, it builds the catalog database, search index, photo lookup, customer profiles, checkout flow, miss log, and admin screens.
Do I get a real app, or a template?
A real app. Kliv builds around your catalog fields, product data, photos, customer records, and checkout rules.
Can I change it after it’s built?
Yes. You can add catalog fields, change search behavior, add review queues, or revise checkout later by asking for changes.
Does my catalog have to live somewhere else?
No. The catalog is rows in your app’s own database, and the photos are files beside them. Search runs where the data lives.
What happens when search finds nothing?
The query is recorded in the ask log, the customer can open a support thread, and a weekly digest can rank the misses for staff review.
How does photo search match parts?
Your product images are described and indexed when uploaded. A customer’s photo is described the same way and compared against your product index.
Can the assistant steer someone to the wrong part?
It is built to answer from your SKU records, fitment map, and notes. When those records do not settle the question, it should say so and open a support thread.
Can customers buy from the result page?
Yes. If you sell products, checkout can run in the same app on your own Stripe account, with the order stored beside the catalog data.
Do my customers need their own Stripe accounts?
No. Customers just pay by card. Payments run through your Stripe account.
Can I use my own domain?
Yes. The store or search app can run on your own domain and go live as soon as you build it.
Can I move off Kliv later?
Yes. The code syncs to your own Git repository, so leaving is a git pull, not a rebuild.
Will product pages show up in search engines?
Yes. Public pages are prerendered for search engines and AI assistants, and the sitemap is generated automatically.
Built by Creators
Explore What's Possible
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Memory Café Coordination Tool
A warm platform for dementia-friendly cafés, connecting caregivers, volunteers, and coordinators.
Remit Record
Track remittances and household budgets seamlessly.
Next Page Book Club
Manage your book club easily with proposals, voting, and history tracking.
Northfern Tattoo Studio
Portfolio and booking site for Northfern Tattoo Studio.
Campo Agua Gestión
Sistema de gestión del agua comunitario para aldeas.
つくりて — 伝統工芸職人サイト
職人が作品を展示し、受注管理を行うサイトです。
Tidal Surf Reports
Crowd-sourced surf condition tracking app.
Riverside Commons HOA Management
A portal for HOA management at Riverside Commons.
よやくん — レストラン予約管理
小規模レストラン向けの予約管理サイトです。
Troop 214 Scout Management
A management tool for Scout Troop 214, focused on outings, advancement, and communication.
Te Kura Kaupapa Māori Whānau Board
A whānau coordination tool for Māori-medium schools.
Tsev Nyeem Ntawv Library
Mobile library coordinator for Hmong and Lao communities.
Let customers search the way they talk
Describe your catalog, the details that prove fit, the photos you have, and what should happen when search misses. Kliv builds the search and discovery app around those records.