Which AI search platforms do you work with?

We implement Algolia, Coveo, Bloomreach, Constructor.io, and Salesforce Einstein Search, as well as platform-native search enhancements for SFCC and Shopify. We also support Searchspring and other category tools. We assess which platform is most appropriate for your catalogue size, commerce platform, and use case requirements.

How long does a search implementation take?

A standard AI search implementation — covering platform integration, catalogue indexing, relevance configuration, and analytics setup — typically takes six to ten weeks. More complex implementations involving personalisation, multiple catalogues, or multi-locale support take longer.

Can you improve our existing search rather than replacing it?

Yes. If you already have an AI search tool installed, we can start with a configuration and performance audit to identify where relevance is breaking down. In many cases, significant improvements can be made through better synonym management, rule configuration, and analytics-driven tuning without a full replacement.

What data does AI search need to personalise results?

At minimum, AI search personalisation requires browse and click behaviour at the session level. For stronger personalisation, purchase history and customer segment membership improve ranking quality significantly. We assess your available data during discovery and design the integration accordingly.

How do you measure the impact of a search improvement?

We measure search impact through a defined set of metrics: click-through rate from search, conversion rate from search, add-to-cart rate, zero-result rate, and search-attributed revenue. We establish baselines before implementation so the improvement can be clearly attributed.

What is faceted navigation and why does it matter for discovery?

Faceted navigation is the system of filters — size, colour, price, brand, attribute — that customers use to narrow down product results. AI-powered faceted navigation surfaces the most relevant filter options dynamically based on query context, reducing the effort required to find the right product. We implement and configure faceted navigation as part of the search engagement.

Do you support multi-language and multi-locale search?

Yes. Multi-language search — including language-specific synonym management, locale-based ranking, and currency handling — is a supported scope for all the platforms we work with. We define locale requirements during discovery and factor them into the integration and configuration design.

AI SEARCH & DISCOVERY

Help Customers Find What They Are Looking For, Faster

We implement, configure, and optimise AI-powered search and discovery tools to improve product findability, reduce search abandonment, and increase conversion across your commerce store.

Improve Your Search Experience

The Problem

Poor Search Costs You Sales You Should Be Winning

THE SEARCH PROBLEM

Search users convert at a significantly higher rate than browsers — but only when search works well. Most ecommerce search fails at the moments that matter most: typos return zero results, long-tail queries surface irrelevant products, and popular searches consistently return the wrong items.

Built-in platform search tools are limited by design. They prioritise exact match over semantic relevance, have no learning mechanism, and cannot be tuned against business goals like margin, conversion, or inventory.

Every failed search is a customer who could not find what they wanted — and many of them leave without buying.

Poor search is invisible to the business — but highly visible to the customer.

Zero-Result Pages

Queries that return no results — due to spelling variations, synonyms, or natural language — result in customer frustration and abandoned sessions.

Irrelevant Ranking

Default search ranking surfaces products by exact keyword match rather than relevance — returning the wrong products for high-intent queries.

No Behavioural Learning

Built-in search tools do not learn from customer behaviour — so popular queries that consistently lead to bounce are never automatically improved.

Invisible Performance Gaps

Teams do not know which searches are failing because there is no analytics layer showing zero-result rates, query exit rates, or conversion by search term.

Our Solution

We implement AI-powered search and discovery tools and configure them to perform against your specific catalogue, customer behaviour, and conversion goals. Our work goes beyond installation — we design the integration architecture, configure relevance rules, connect customer data, and establish an ongoing tuning process that improves performance over time.

Search Audit

We review your current search performance — analysing zero-result rates, query patterns, conversion by search, and existing tool configuration.

Platform Selection

Where required, we evaluate and select the right AI search platform for your catalogue size, commerce platform, and use case requirements.

Integration & Indexing

We integrate the search platform with your commerce stack — connecting the product catalogue, attribute data, and customer behaviour signals.

Configuration & Tuning

We configure relevance rules, synonyms, query rules, and merchandising logic — tuning the search experience to your catalogue and customer intent.

Personalisation Layer

Where applicable, we connect customer data to enable personalised result ranking based on individual browse and purchase behaviour.

Monitoring & Improvement

We establish search analytics reporting and a regular relevance review cadence — continuously improving performance based on customer query data.

What We Deliver

From Implementation to Ongoing Relevance Tuning

Search Platform Implementation

Implement and integrate AI-powered search tools — connecting them to your product catalogue, customer data, and commerce platform.

Relevance Configuration

Configure ranking algorithms, synonym libraries, query rules, and merchandising rules to improve search result quality for your catalogue.

Personalised Search

Connect customer behaviour and purchase history to personalise search result ranking for individual customers and segments.

Faceted Navigation

Design and implement AI-powered faceted navigation and category filtering — improving product discoverability beyond the search bar.

Analytics & Reporting

Configure search analytics to surface zero-result queries, high-exit searches, and conversion drop-off points — informing ongoing optimisation.

Continuous Tuning

Establish a regular relevance review process to improve search performance as your catalogue, customer base, and seasonal context evolves.

Use Cases

Search Improvements That Drive Commerce Outcomes

Replacing Weak Native Search

Replace an underperforming native commerce search tool with an AI-powered platform that handles natural language, typos, and long-tail queries.

Personalised Search Results

Connect customer data to search ranking so returning customers see results shaped by their own purchase and browse behaviour.

Category & Discovery Pages

Apply AI-powered sorting and faceted navigation to category pages — improving product discovery beyond the search bar.

Zero-Result Optimisation

Identify and address the queries that consistently return zero results — through synonym expansion, redirect rules, or AI query understanding.

Merchandising-Led Search

Configure search to factor in business rules — boosting margin products, surfacing in-stock items, and promoting campaign priorities alongside relevance ranking.

Multi-Locale Search

Implement search across multiple languages, locales, and catalogues — with locale-specific synonym management and relevance tuning.

Search Platforms We Work With

AI-Powered Search Across Commerce Platforms

Algolia

Implemented

Coveo

Implemented

Salesforce Einstein Search

Implemented

Bloomreach

Implemented

Why Innovadel

Search Implementations That Perform and Improve

Deep Search Platform Knowledge

We have hands-on implementation experience with the leading AI search platforms — including Algolia, Coveo, Bloomreach, and Salesforce Einstein — across SFCC, Shopify Plus, and Adobe Commerce environments.

Beyond Installation

We go beyond connecting the API. We configure relevance rules, tune ranking logic, design analytics reporting, and establish a tuning process — so search performs well at go-live and improves over time.

Conversion-Focused Outcomes

We measure search implementations against conversion and revenue metrics from the start — not just technical uptime — so the business impact is visible and attributable.

Engagement Models

Search Audit & Recommendations

Best for teams with an existing search tool that is underperforming and need a diagnosis and improvement plan.

Platform Implementation

Best for businesses implementing a new AI search platform or replacing native commerce search.

Personalisation Add-On

Best for teams with an existing search tool who want to add personalised result ranking based on customer behaviour.

Optimisation Retainer

Best for live search implementations that need ongoing relevance tuning, query analysis, and performance improvement.

Is poor search costing you conversion?

Talk to Innovadel about an AI search implementation that improves findability and conversion.

Improve Your Search Experience Explore All Services