How is AI search different from a regular site search?

Standard search looks for word matches: if a word from the query isn't in the listing, there's no result. AI search understands meaning: a query like "warm jacket for rain" finds a water-resistant windbreaker even if the exact words aren't in the listing. It handles synonyms, purpose-based descriptions, and typos — exactly where standard search comes up empty.

Can you connect AI search to our store?

Yes — we index your catalog and embed the search into your site. We work with standard e-commerce platforms and custom builds: as long as there's a way to access the catalog (API, export, database), we can connect it. This can be part of a new e-commerce project or a standalone integration into an existing site.

How much does AI search implementation cost?

It depends on the catalog size, description quality, and integration with your site. We give an exact range after the breakdown: show us your catalog and problem queries and we'll tell you what AI search would deliver and what it would cost. Before that, any number would just be a guess.

Who doesn't need AI search?

If the catalog is small — a few dozen items — standard search and filters already work, and paying for more doesn't make sense. Same with knowledge bases: if there's little material and it rarely changes, a simple FAQ is cheaper. AI search pays off with volume and variety of queries. If that's not your case, we'll say so honestly during the breakdown.

Will the search invent products that don't exist?

No. AI search only works with your index: it finds and ranks what's actually in your catalog or knowledge base — it doesn't generate text. If nothing is relevant, it shows the closest match by meaning, and that's visible in the results. Fabrication is a risk of generative answers; here the model searches, it doesn't write.

Service

AI search: the client describes it in words — and finds it

Standard search looks for exact word matches. A client types "spring jacket with a hood," but in your catalog it's listed as "transitional-season windbreaker" — they see "no results" and leave. AI search understands the meaning of the query, not the exact letters, and finds what they need. Stack: Node.js + OpenAI / Anthropic embeddings.

Where AI search works

Three places where search loses clients

Catalog search
The client describes a product in their own words — by purpose, feature, or situation — not the exact name on the listing. AI search understands the intent and shows relevant products instead of an empty result. Fewer "no results" screens, more clicks into the cart.
Fits: e-commerce with a few hundred SKUs or more, where clients search in varied ways.Doesn't fit: a small catalog where standard search and filters already work fine.
Knowledge base search
A client or employee asks a question in everyday language — AI finds the answer in your guides, FAQs, contracts, and documents. Not a list of articles that "might" have the answer, but the specific passage that matches the question.
Fits: schools and courses, support teams, service companies with a large body of materials.Doesn't fit: few materials that rarely change — a simple FAQ is cheaper.
Internal search
The team searches through its own documents, procedures, project history — spending minutes on something that should take seconds. AI search pulls the right passage based on the meaning of the query, not tag matches.
Fits: companies with an accumulated document base that employees dig through constantly.Doesn't fit: few documents with a clear structure — folders and titles do the job.

How it works

Search by meaning, not by exact word

What's included

Intent, not letters. "Something warm for a rainy autumn" finds water-resistant jackets even if the listing doesn't contain any of those words. The search handles synonyms, purpose-based descriptions, and typos.
Results instead of emptiness. If there's no exact match, AI search shows the closest match by meaning instead of "no results." The client stays on the site and sees an alternative.
Works with your data. We index your actual catalog or knowledge base — descriptions, specs, documents. Nothing is invented: the search only returns what you actually have.
Updates without retraining. Add products or a new document and the index updates — search finds them immediately. No "fine-tuning" required.
Query log. See what people searched for and where the search came up empty — direct signals about which products or answers you're missing.
Stack: Node.js + OpenAI / Anthropic embeddings. Integrates into your site or system. Data is processed under enterprise API terms — it's not used to train public models.

How we work

First on your catalog — then live

Catalog and query breakdown
We look at your catalog or knowledge base: how many items, how they're described, how clients currently search, where search returns empty. Deliverable: an assessment of what AI search would improve, where the biggest gains are, timeline and budget range.
Pilot on your search
We index your real catalog and build a working search. We run your own problem queries through it — the ones that currently return "no results." Deliverable: a working search on your catalog + a before/after results comparison on real queries.
Integration into your site or system
We embed the search into your site or interface and set up index updates when the catalog changes. We test at full scale. Deliverable: search live on your site + automatic index updates + a query log for analysis.
Launch and tuning from the log
We go live and spend the first weeks reviewing the log together: which queries still come up empty, where results are off-target, what to adjust. Deliverable: search in production + a list of queries missing products or answers + a support agreement.

FAQ

What people ask about AI search

Ask about AI

Standard search looks for word matches: if a word from the query isn't in the listing, there's no result. AI search understands meaning: a query like "warm jacket for rain" finds a water-resistant windbreaker even if the exact words aren't in the listing. It handles synonyms, purpose-based descriptions, and typos — exactly where standard search comes up empty.
AI search finds and shows: it returns relevant products or passages to the client, and the decision stays with them. An AI agent goes further — it reads the knowledge base and formulates the answer itself, or takes an action (replies in chat, creates a task, logs into the CRM). If you need the system to answer rather than just find, that's an AI agent — see the relevant service.
Yes — we index your catalog and embed the search into your site. We work with standard e-commerce platforms and custom builds: as long as there's a way to access the catalog (API, export, database), we can connect it. This can be part of a new e-commerce project or a standalone integration into an existing site.
It depends on the catalog size, description quality, and integration with your site. We give an exact range after the breakdown: show us your catalog and problem queries and we'll tell you what AI search would deliver and what it would cost. Before that, any number would just be a guess.
If the catalog is small — a few dozen items — standard search and filters already work, and paying for more doesn't make sense. Same with knowledge bases: if there's little material and it rarely changes, a simple FAQ is cheaper. AI search pays off with volume and variety of queries. If that's not your case, we'll say so honestly during the breakdown.
No. AI search only works with your index: it finds and ranks what's actually in your catalog or knowledge base — it doesn't generate text. If nothing is relevant, it shows the closest match by meaning, and that's visible in the results. Fabrication is a risk of generative answers; here the model searches, it doesn't write.

Discuss your project

Send us your catalog — we'll show search on your queries

30 minutesOne-on-one online
Flexible formatVideo or phone call
Solution-focusedPractical answers

More detail

Why "no results" without AI search is costing you sales

AI search (semantic, or vector search) is search that finds results by the meaning of the query, not an exact word match. Apricode — a digital agency from Kharkiv, in web development since 2016, working remotely with clients worldwide — builds it on Node.js with OpenAI and Anthropic embedding models. Technically: every product and query is converted into a vector of meaning, and the search returns the closest matches by meaning rather than identical spelling.

What an empty result actually costs

Imagine a clothing store. A client searches for "spring jacket with a hood," but the same item is listed as "transitional-season windbreaker." Standard search sees no word match and returns "no results." The client doesn't scroll through the catalog manually — they close the tab and buy from a competitor. That doesn't cost you one product, it costs an entire category of queries where the buyer's words and the listing's words simply didn't line up. AI search closes that gap: it understands intent and shows what the client actually meant.Turnkey e-commerce development →

The same logic applies to knowledge bases. Online schools, technical support teams, and service companies accumulate dozens of guides, contracts, and answers. A person asks a question in their own words, while standard search expects them to guess the exact term used in the article. AI search finds the answer by the meaning of the question and surfaces the exact passage. Fewer support requests for things already covered in the knowledge base — less load on the team.

Search or agent: where's the line

It's important not to confuse the two. AI search finds and shows: it returns relevant products or passages from your knowledge base, and the decision stays with the person. A RAG-based AI agent goes further — it reads your knowledge base and formulates the answer itself, or takes an action: replying in chat, creating a task, logging into the CRM. Search is cheaper and simpler where finding is enough. An agent is needed where the system also has to act. It's often sensible to start with search and grow into an agent later.AI agents for business →

Let's be honest: AI search isn't for everyone. If you have a few dozen products and clear filters, standard search already works, and the budget is better spent elsewhere. AI search pays off with volume and variety of queries: a large catalog, an active knowledge base, many ways to describe the same thing. We apply the same embeddings and RAG approach in our AI document processing and in the AI module of our Apros CRM — this is a technology we've already proven, not an experiment on your budget.AI document processing →

We'll show AI search on your actual catalog: you give us real listings and the queries that currently return "no results," and we build a pilot to compare results before and after. If you'd rather see the full range of our AI work first, start with an overview of our AI solutions for business.AI for business: overview →

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