Semantic Search Engine

Find what your users are looking for, even if they can't express it exactly

WhatsApp
Agent sheet
Category
Search and structured data
Model
open, installed on your premises
Data
never leaves
Subscription
€0 per month
01Overview

Exact keyword search is dead. Your users type intentions, not keywords. A catalogue of 10,000 products with exact search returns zero results for "comfort long-walk shoe" if the product is called "premium orthopedic insole". The semantic search engine understands the intent behind the query and returns the most relevant results, even with different terms.

The problem

Keyword-based search engines have a zero-result rate of 20–40% on e-commerce catalogues. Users abandon after two failed attempts. Poor internal search costs lost sales and a bad user experience.

Our answer

Your content (products, articles, documents) is encoded as semantic vectors and stored in Pinecone, Weaviate or Elasticsearch. With each search, the query is encoded the same way and the semantically closest results are returned, regardless of the exact words used. Hybrid re-ranking (semantic + popularity + business filters) refines relevance.

02How it works

Live in four steps.

  1. 01
    Content indexing

    Import and vector encoding of your catalogue, knowledge base or document corpus. PDF, HTML, JSON, CSV supported.

  2. 02
    Engine configuration

    Embedding model selection (OpenAI, Cohere, sentence-transformers), vector database setup and business filters.

  3. 03
    Interface integration

    REST API or JavaScript SDK to integrate the engine into your site, mobile app or internal tool. Response time < 200ms.

  4. 04
    Continuous optimization

    Analysis of zero-result queries, embedding adjustment, A/B testing of re-ranking strategies.

03What you gain

Results you can measure.

Zero-result rate < 2%

Semantic search understands intent and always returns relevant results, even for imprecise queries.

Conversion +25%

Users who find what they're looking for buy. Relevant search is one of the best e-commerce conversion levers.

Real-time updates

Every new product or document is indexed in seconds. The engine always reflects your current catalogue state.

04Frequently asked

What we get asked before signing.

What's the difference from classic Elasticsearch?
Classic Elasticsearch does exact term matching. Semantic search understands intent. Both can be combined (hybrid search).
How many documents can be indexed?
From a few thousand to several million documents depending on configuration. Modern vector databases scale horizontally.
Does the engine support multiple languages?
Yes. Multilingual embedding models (like multilingual-e5) enable consistent cross-language search.
What is the search latency?
Under 200ms for most configurations. Additional optimizations can bring this below 50ms if needed.

Let's talk about your case for thirty minutes.

A conversation to understand your context, volumes and tools. You leave with a scope and an order of magnitude, no commitment.

WhatsApp
+261 38 66 261 00team@wikolabs.com