Why Custom Search & Merchandising Engines Outperforms Native Coveo Features

When enterprise e-commerce platforms and omnichannel retailers evaluate their technology stacks, algorithmic search functionality is rarely considered a peripheral feature. It is a core driver of revenue. As a business scales, the default search experiences provided by storefronts or basic implementations of enterprise search platforms often begin to show their limitations. Evaluating when to build vs buy for Search & Merchandising Engines is one of the most critical architectural decisions an operations team will face. While foundational platforms are powerful, understanding Coveo limitations in advanced e-commerce scenarios is crucial before embarking on a massive overhaul. This is precisely when evaluating Custom Search & Merchandising Engines becomes essential to maintain a competitive edge and optimize conversion rates.

Many businesses turn to robust enterprise search solutions like Coveo to handle their vast product catalogs and content. Coveo offers extensive capabilities, but as organizations demand highly specific business rules—such as boosting products based on real-time profit margins, managing complex B2B entitlements, or handling bespoke algorithmic search requirements—the standard implementation begins to fracture. The symptoms often present as performance bottlenecks, but the root cause is rarely the platform itself; it is a business process mismatch. When operations outgrow native features, investigating dedicated Custom Search & Merchandising Engines often becomes a strategic necessity rather than just a technical upgrade.

The True Source of Search Latency and Bloated Page Weights

A common misconception when discussing Coveo limitations is attributing poor performance or slow page load times directly to the engine's native capabilities. When storefronts experience bloated page weight and unacceptable latency during search queries, it is almost always due to how the data is being requested and processed.

Instead of leveraging the engine's native indexing and querying mechanisms efficiently, organizations often build complex middle-tier applications. These middleware layers intercept the user's search query, fetch data from various backend systems (like an ERP such as NetSuite or a PIM), and dynamically construct massive JSON payloads containing highly customized, hardcoded queries. These complex custom queries are then passed to the search engine, forcing it to process an incredibly convoluted instruction set on the fly.

The result is not a failure of the search engine, but a failure of the architecture. The search engine is forced to parse and execute logic that should have been resolved during the indexing phase. This middle-tier approach fundamentally undermines the speed and efficiency that platforms like Coveo are designed to provide. When developers and architects mistakenly blame the search engine for latency, they often embark on unnecessary platform migrations rather than addressing the structural flaws in their middleware.

Algorithmic Search and the Cost of Custom Merchandising

When evaluating custom search and merchandising engines against off-the-shelf implementations, it is essential to distinguish between core text retrieval and business logic. Core text retrieval—which includes handling synonyms, typo tolerance, and basic relevance—is handled natively and exceptionally well by foundational search technologies like Elasticsearch.

The true custom cost does not lie in building a better typo-tolerance algorithm. Instead, the cost and complexity arise when building middle-tier applications to construct custom queries for business rules. For example, if your business requires dynamic merchandising that boosts or buries products based on real-time inventory levels in specific warehouses, shipping costs via providers like Sendle or ShipStation, or current profit margins, relying on simple native features is often insufficient. Building custom applications to weave this business data into Elasticsearch queries requires significant development overhead and ongoing maintenance.

Modern dedicated Search and Merchandising engines, such as Algolia or Constructor.io, approach this differently. It is important to note that these dedicated engines often use their own proprietary search architectures for core text retrieval (for instance, Algolia relies on a highly optimized C++ engine) rather than simply wrapping Elasticsearch. They provide sophisticated visual editors and dynamic rule engines out-of-the-box, allowing business users to implement complex merchandising strategies without requiring developers to write hardcoded queries in a middleware layer. This decoupling of business logic from technical implementation represents a massive leap in operational efficiency.

Native Limitations vs. Business Requirements

When companies attempt to use native Coveo features to handle highly bespoke merchandising, they often encounter architectural friction. Coveo is a powerful unified enterprise search platform that natively offers robust AI-driven recommendations and basic e-commerce merchandising tools. However, Coveo limitations become apparent when businesses require deep, granular control over the core algorithmic search logic or highly complex, multi-variable rule sets tailored to specific operational metrics. In these edge cases, forcing Coveo to act as a hyper-specialized engine can lead to technical debt.

This is especially true when integrating with comprehensive ecommerce platforms like Shopify or BigCommerce. It is crucial to classify Shopify and BigCommerce accurately: they are robust ecommerce platforms and storefronts that provide comprehensive backend e-commerce capabilities, even when used headlessly. They are not merely frontend platforms. When you integrate a search engine with these robust platforms, the integration must respect the platform's backend capabilities. If your custom merchandising logic is hardcoded into a middleware layer instead of being natively understood by the search index, you create a brittle architecture that is difficult to maintain and scale. Merchandising teams need the agility to react to market trends instantly, and waiting for an engineering sprint to update a hardcoded query is a competitive disadvantage.

The Wilson Tech Approach

The classic tech fix for search limitations is to add more computing power to the middle-tier, write more complex GraphQL or REST queries, or attempt to force the existing search platform to execute business logic it was never designed to handle. This approach treats search latency and merchandising inflexibility as a purely technical glitch, leading to endless optimization sprints that yield minimal improvements. It focuses on maintaining the status quo rather than addressing the underlying process inefficiencies.

At Wilson Technology, we take a different path. The Wilson Tech Approach begins by analyzing the entire operational lifecycle and understanding the underlying business process. We view search not just as a technical function, but as a critical business capability that must align with your revenue goals. We know that a slow search experience directly correlates to lost conversions, and that inflexible merchandising prevents sales teams from moving high-margin inventory effectively.

Instead of building fragile middle-tier applications that construct massive custom queries on the fly, we evaluate your true business needs to determine the optimal architecture. If your business requires dynamic, rule-based merchandising driven by margin and inventory, we focus on structuring your data and indexing strategy so that the search engine can do what it does best: retrieve information instantly. Whether this means optimizing your current Coveo implementation by moving logic to the index, or migrating to a dedicated custom search and merchandising engine that aligns with your operational goals, our focus is on enabling business value rather than just deploying technological capability. We build solutions that reduce costs, eliminate middleware bottlenecks, and empower your merchandising teams to operate autonomously.

Evaluating the Build vs. Buy Decision

The decision to invest in custom search and merchandising engines rather than relying on native features depends heavily on your team's agility and your business model's complexity.

1. When to Optimize Native Features: If your merchandising rules are relatively static, and your primary goal is unifying content across customer service portals and your catalog, leveraging and correctly indexing data for your existing enterprise search platform is often the most cost-effective route. If your operations team does not need to intervene in the search algorithm on a daily basis, forcing a migration may introduce unnecessary risk.
2. When to Choose Dedicated Merchandising Engines: If your revenue relies heavily on algorithmic search that requires daily adjustments by non-technical merchandisers—such as personalized B2B catalogs, dynamic pricing, and inventory-aware boosting—investing in a specialized search and merchandising engine is typically the better choice. It eliminates the need for expensive middle-tier custom development and puts the power directly in the hands of the business. The return on investment is realized through increased conversion rates and reduced reliance on engineering resources for routine merchandising tasks.

Conclusion

Search is the digital equivalent of a knowledgeable sales associate; it must be fast, accurate, and aligned with your business objectives. Struggling with bloated page weights and rigid merchandising is a clear sign that your architecture is hindering your operations. By addressing the root cause—often complex custom queries hardcoded in middleware—you can unlock the true potential of your e-commerce platform. If you are evaluating how to streamline your search architecture and improve your merchandising capabilities without falling into the trap of endless custom development, consider reviewing your data indexing strategy first and ensuring your technology aligns with your business goals. For further guidance on aligning your technology stack with your business objectives, reach out to Wilson Technology to explore holistic solutions tailored to your unique operational needs.