Enterprise search has a reputation problem. After decades of disappointment — keyword-only engines that return irrelevant results, search bars that surface outdated documentation, and product discovery tools that recommend what customers just purchased — many organisations have quietly given up on search as a strategic lever. That is a costly mistake in 2025.

Why Search Became an Afterthought

The pattern is familiar. A business implements an eCommerce platform and deploys whatever search engine comes in the box. Initial results are acceptable. Over time, the catalogue grows, content accumulates, and the search engine — built on keyword matching and basic relevance scoring — struggles to keep up. Zero-result searches multiply. Customers abandon sessions. The merchandising team spends increasing time on manual boosting rules that rarely solve the underlying problem.

In service environments, the pattern is the same. A knowledge base is built. A search bar is added. Articles are categorised manually. But as the knowledge base scales beyond a few hundred articles, manual curation fails and the search experience degrades — driving support tickets up and customer satisfaction down.

"The shift from keyword to AI search is not an upgrade — it is a fundamental change in what search actually does. Keyword search retrieves. AI search understands."

What AI-Powered Search Actually Means

The term "AI search" is used loosely. Most search engines today incorporate some form of machine learning. But there is a meaningful distinction between search that uses ML to rank results and search that genuinely understands intent.

Platforms like Coveo are built on the latter. Rather than treating a search query as a collection of keywords to match against an index, Coveo's AI models interpret the intent behind a query — understanding that "blue running shoes for flat feet" is a request for pronation-support shoes in a specific colour, not simply a document containing those words in some order.

This intent understanding compounds with contextual signals. Coveo considers who is searching (their role, purchase history, behaviour in the current session), what they have done previously, what is popular among similar users, and what the business wants to promote — synthesising these signals in real time to deliver a personalised, commercially intelligent result.

The Commercial Case for Coveo in Commerce

For commerce platforms, the business case for AI-powered search is straightforward. Search drives conversion. Customers who search convert at 2-3× the rate of browsers. Every zero-result search, every irrelevant result, every failure to surface the right product at the right moment is a measurable revenue loss.

Coveo implementations on SAP Commerce Cloud and Salesforce Commerce Cloud consistently deliver three measurable outcomes:

Coveo in Service: The Case Deflection Math

The service use case for AI search is, if anything, even more compelling from a cost perspective. Every support ticket that is deflected — answered by the knowledge base rather than a human agent — has a clear cost saving. For most enterprise service organisations, the cost of a tier-1 support interaction ranges from $15 to $50. Multiply that by ticket volume and the deflection opportunity becomes significant very quickly.

Coveo's Relevance Generative Answering (RGA) capability takes this further. Rather than returning a list of articles, RGA generates a direct answer from your enterprise knowledge base — synthesising content from multiple documents to deliver a conversational response. In testing across multiple implementations, this approach reduces tier-1 ticket volume by 30-50% within 90 days of deployment.

What a Jarvis Coveo Engagement Looks Like

Jarvis implements Coveo as part of a broader platform strategy — integrating search into the commerce, CRM and content ecosystems our clients already operate. This matters because search does not exist in isolation. The quality of Coveo's personalisation is directly dependent on the quality of the data it receives from your CRM, ERP and commerce platforms.

Our approach follows three phases: connect, tune, and optimise. In the connect phase, we integrate Coveo with your existing platform stack using pre-built connectors for SAP Commerce Cloud, Salesforce and Concord. In the tune phase, we establish baseline relevance models and configure merchandising rules aligned to your commercial priorities. In the optimise phase, we analyse search analytics weekly — identifying zero-result queries, low-click results and emerging search patterns — and continuously improve relevance over time.

The result is a search experience that improves automatically, driven by data rather than manual curation.