AI Powered Search

In modern e-commerce, search is no longer limited to simple keyword matching. Users expect a search experience that understands intent, interprets context, and delivers relevant results even when queries are incomplete, ambiguous, or informal. AI Powered Search is designed to elevate the in-shop search experience by leveraging artificial intelligence to meet these expectations.

With AI Powered Search, users can:

• Enter queries with typos or misspellings,

• Use synonyms or alternative expressions instead of exact product names,

• Describe their needs using natural, free-form language, for example: “I’m looking for a birthday gift for my nephew”,

and still receive accurate and relevant product results. The AI analyzes these inputs to understand the underlying intent, rather than relying solely on exact keyword matches.

By combining natural language understanding with product catalog data, category structures, and semantic relationships, AI Powered Search delivers results that are aligned with what the user actually wants to find.

Monitoring, Control, and Continuous Optimization

AI Powered Search is not a black-box solution. Through Omnitron, teams can:

• Monitor search queries and user behavior,

• Analyze frequently searched terms and phrases,

• Identify zero-result or low-performance searches,

• Track conversion and engagement metrics,

• Configure and manage AI search plans that influence how results are generated.

This level of visibility allows brands to:

• Measure search performance using clear metrics,

• Fine-tune AI behavior based on business goals,

• Continuously improve the overall search experience.

Scope of This Tutorial

In this tutorial, you will learn:

• How AI Powered Search works at a conceptual level,

• How user search inputs are interpreted and processed,

• Which metrics are collected and how they can be analyzed,

• How AI Powered Search–related screens in Omnitron are structured,

• How to configure and manage search plans and related settings.

In the next section, we will walk through the AI Powered Search in Omnitron, explaining how each configuration and monitoring tool can be used to manage and optimize the search experience.

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AI Powered Search in Omnitron

Omnitron provides a set of dedicated screens to monitor, analyze, and configure the AI Powered Search experience. These screens allow teams to understand how users search within the shop, evaluate search performance through metrics, and fine-tune AI behavior using configurable search plans and keywords.

The AI Powered Search module in Omnitron consists of four main screens:

• Dashboard

• Metrics

• Keywords

• Search Plans

Each screen focuses on a different aspect of the search lifecycle—from high-level visibility to detailed configuration and performance analysis.

We will start with the Dashboard, which offers a comprehensive overview of search activity at a glance.

1. Dashboard

The Dashboard provides a high-level summary of how AI Powered Search is being used in the shop. It is designed to give quick insights into overall search activity, trends, and user behavior without requiring deep configuration or analysis.

Key Metrics

At the top of the Dashboard, three core metrics are displayed:

Today’s Searches: Shows the total number of searches performed in the shop today. This metric also includes a percentage comparison with the previous day, displayed as “Compared to yesterday”, allowing you to quickly assess daily search activity trends.

Total Keywords: Displays the total number of defined Keywords that are currently configured and used by the AI Powered Search engine.

Search Plans: Indicates the total number of configured Search Plans, which define how search queries are interpreted and how results are generated.

Search Distribution

Below the top metrics, the Distribution chart visualizes how search activity is spread over time. This chart allows you to analyze search behavior at different time granularities, including:

• Monthly

• Weekly

• Daily

• Hourly

By switching between these views, you can identify peak search periods, seasonal patterns, and user activity trends throughout the day.

Popular Searches

The Popular Searches table lists the top 10 most frequently searched text queries across the shop. This section helps you understand what users are actively looking for and can be used to:

• Identify high-demand products or categories,

• Detect recurring search intents,

• Inform keyword definitions and search plan optimizations.

Language Distribution

The Language Distribution chart shows the languages in which users perform their searches. This visualization is particularly useful for multi-language shops, as it enables you to:

• Understand language-based search behavior,

• Evaluate whether search configurations are aligned with active languages,

• Identify opportunities for improving keyword coverage or AI understanding in specific languages.

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2. Metrics

The Metrics page provides detailed visibility into what users search for in the shop and how often. Every search query entered by a user is recorded by a scheduled task that runs once per minute, ensuring that search activity is captured continuously and consistently.

This page is primarily used to analyze search behavior over time, identify high-frequency queries, and evaluate trends that can be used to improve search plans and keyword configurations.

Metrics Table

Each record in the Metrics table represents a grouped view of searched text, enriched with time-based information. The table includes the following columns:

Search Text: The exact text entered by the user in the shop search bar.

Count: The total number of times the search text was queried within the selected time grouping.

Time Range Start: Indicates the starting point of the time range from which the displayed count is calculated.

First Search Time: Shows the earliest time at which the search text was queried within the selected grouping.

Time-Based Grouping (Group by)

The values in the Count, Time Range Start, and First Search Time columns are directly affected by the “Group by” filter located in the top-right corner of the page.

The Group by option allows search queries to be grouped into different time intervals:

• 1 minute

• 5 minutes

• 1 hour

• 1 day

• 1 month

This grouping enables flexible analysis of search data at different time resolutions.

Example:

If the search text “milk” is grouped by 1 Hour, the Metrics table will show:

• How many times “milk” was searched during that hour (Count),

• The first time it was searched within that hour (First Search Time),

• The start date of the month from which the count is calculated (Time Range Start).

By changing the grouping level, the same search text can be analyzed minute-by-minute, hourly, daily, or monthly.

Filtering and Sorting

The Metrics page also supports advanced filtering and sorting to help you focus on specific data points:

• Filters can be applied to narrow down metrics based on search text and other available criteria.

• Sorting options in the table allow you to order records by:

  • Count (ascending or descending),

  • Time-related fields (ascending or descending).

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3. Keywords

The Keywords page is used to convert real user search behavior into structured search intelligence. Its main purpose is to transform the search texts collected on the Metrics page into reusable and configurable Keywords that can be leveraged by AI Powered Search and Search Plans.

By defining keywords based on actual user searches—such as misspellings, alternative expressions, or commonly used phrases—you can guide the AI to better understand user intent and return more relevant results.

Creating a New Keyword

To create a new keyword, click the + Add New Keyword button located in the top-right corner of the page. This action opens the keyword creation form.

Keyword Form Fields:

Keyword: This field contains the keyword text itself. Typically, this value is derived from the Metrics page and may represent:

• A frequently searched term,

• A misspelled word,

• A commonly used alternative expression.

Example: Users may search for “garden furniture”, “garden chair”, or similar variations. These frequently searched texts can be selected from the Metrics page and defined as keywords.

They may also enter misspelled queries such as “gardan furnitre” or “garden furnitur”, or use synonyms like “outdoor furniture” instead of “garden furniture”.

By defining these variations as keywords, AI Powered Search can return relevant results even when users use different wording or make spelling mistakes.

Language: Specifies the language in which the keyword is defined. This ensures that the keyword is evaluated correctly within multi-language search scenarios.

Type: Determines how the keyword interacts with other keywords and search plans.

There are two available types:

1) Normal

• The keyword is evaluated independently.

• It is matched directly against search plans that explicitly include it.

• Suitable for specific, well-defined terms with limited contextual variation.

2) Contextual

• The keyword can be combined with multiple other keywords.

• The AI establishes a contextual relationship between this keyword and others that appear together in search queries.

• Especially useful for generic or commonly reused terms.

Example: Consider the keyword “garden furniture”:

• There may already be keywords related to “garden” and “furniture”.

• Separate search plans might exist for garden-related products and furniture-related products.

When Contextual is selected:

• The AI can associate this keyword with both garden-related and furniture-related search plans.

• Searches such as:

  • “garden lighting”

  • “garden soil”

  • “garden chair” can still take this keyword into account.

• Similarly, for furniture-related searches, variations such as:

  • “bathroom furniture”

  • “kitchen furniture”

  • “baby furniture” can also be evaluated using contextual relationships.

In this way, Contextual keywords enable the AI to find relevant search plans for derived or combined search queries, even when those exact combinations were not explicitly defined.

Term Quantity: Represents the number of terms (words) that make up the keyword.

For example:

• “furniture” → 1 term

• “garden furniture” → 2 terms

This information helps the AI better interpret keyword structure and relevance.

Keyword List

Once keywords are created, they are listed in the Keywords table. In addition to the properties defined during creation, the table includes an additional column:

• Search Plans: Indicates how many Search Plans currently include this keyword.

This column helps you understand:

• The impact scope of a keyword,

• How widely it is used across search configurations,

• Whether a keyword is actively contributing to search logic or still unused.

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4. Search Plans

The Search Plans page is where keywords are turned into actionable search logic. Using the keywords defined on the Keywords page, Search Plans determine how a user’s search input is interpreted, which query is executed, and which filters are applied to generate the final product results.

In short:

• Keywords represent what users type,

• Search Plans define how the system searches.

Search Plans can be created manually by users or automatically by AI based on real search behavior.

Creating a Search Plan

To create a new search plan, click the + Add Search Plan button in the top-right corner of the page. This opens the Search Plan creation form.

Search Plan Form Fields:

Keyword: Select one of the keywords defined on the Keywords page. The keyword can be searched and selected from the list.

This keyword acts as the trigger for the search plan.

Search Query: Defines the actual query that will be executed when the selected keyword is detected.

This field can be populated in two different ways:

• Manually, by entering the desired search query directly.

• Automatically using Create with AI, which analyzes the selected keyword and generates:

  • An appropriate Search Query, and

  • Relevant Filters based on the detected intent.

The Search Query typically represents the corrected, normalized, or intended version of the keyword.

Examples:

• Keyword: “gadrn” → Search Query: “garden”

• Keyword: “gardenfurniture” → Search Query: “garden furniture”

• Keyword: “hoodie” → Search Query: “sweatshirt”

The goal is to ensure that variations, typos, or alternative expressions are mapped to a clean and meaningful search query.

AI-Generated Search Plans (Create with AI): When Create with AI is used, the AI does more than generate the search query. It also automatically adds filters to the search plan by analyzing the structure and intent of the keyword.

Example: If a user searches for “Black M T-Shirt”, the AI may:

• Identify “black” as a color,

• Identify “M” as a size,

• Identify “T-Shirt” as a product category,

and automatically generate a search plan that includes:

• A filter for the color attribute with the value “black”,

• A filter for the size attribute with the value “M”,

• A filter for the T-Shirt category.

Search plans created using this method are listed with Mode = AI in the Search Plan List.

Manually Created Search Plans: If the Search Query and filters are entered manually via the form, the search plan is considered user-defined and is listed with:

• Mode = Human

Priority: Determines the execution priority of the search plan.

When multiple search plans match the same user query, the plan with the higher priority is applied first. This allows you to control which logic takes precedence in overlapping scenarios.

Filters: Filters allow you to extend or refine search results by applying additional constraints based on attributes or categories.

Each filter consists of:

• Filter Key

• Filter Value

These filters are applied alongside the search plan to produce more accurate results.

Examples:

• If the search query is “garden”, a filter can be added to search within the garden category by specifying the category key and value.

• If the Search Query is “red shoes”:

  • A filter can be added for the color attribute with the value “red”,

  • Another filter can target the shoes category.

Conclusion

With the combination of Metrics, Keywords, and Search Plans, AI Powered Search in Omnitron provides a complete and controllable search optimization framework.

• Metrics enable you to observe real user behavior by tracking every search query over time.

• Keywords transform these raw search texts into structured signals that the AI can understand, including misspellings, synonyms, and contextual terms.

• Search Plans convert keywords into actionable search logic, defining how queries are executed, which filters are applied, and which results are prioritized—either manually by users or automatically by AI.

This layered approach ensures that:

• Users receive relevant results even when they make spelling mistakes, use different wording, or express intent in natural language.

• Search behavior is measurable through clear metrics.

• AI-driven decisions remain transparent, configurable, and aligned with business goals.

By continuously monitoring metrics, refining keywords, and optimizing search plans, teams can iteratively improve the shop’s search experience, increase discoverability, and ultimately drive higher engagement and conversion rates.

AI Powered Search in Omnitron is not just a smarter search—it is a search system that learns, adapts, and evolves with your users.