Search matters. In fact, it is the missing piece to your enterprise architecture strategy. It is fundamental to your digital transformation plans. And without great search relevance, your AI projects — generative AI, agentic AI, etc. — plans will fail, full stop.
But no one cares about search. At the C-suite and management level at least. It is seen as a lowbrow technology, not understood as an experience. Search is both ubiquitous and perplexing.
All of this is interesting when we consider that search is pervasive across our digital experiences. AI is a technique for making search work at scale — faster, better, with bigger data sets, less manual tuning, and across formats.
Search is Ubiquitous Yet Forgotten
Think about how much you search in your personal life — on web search engines, email servers, social media, digital maps, your contacts, within messaging apps like iCloud or WhatsApp. And then think about how much you search for work — on the internet while learning/researching, in apps like Word and Google Docs, specific systems like Salesforce, ServiceNow or SharePoint, within Slack or Teams, your email, wikis like Confluence, etc.
Searches are insight-filled activities, signaling what someone wants or needs. If your company is product-focused, content-heavy, or knowledge-based, a search on your site represents a high-intent inbound opportunity for revenue, cost reduction or customer loyalty.
This means you need to raise the bar on search. It’s more than just a box — it’s your window to your customer, offering a 1:1 conversation at scale. It is a medium for providing smart, unified experiences to visitors such as AI-guided recommendations, generative answering, relevant results — all of which impact a visitors’ perception of your brand and their decision to do business with you. Or not.
Search is a core, fundamental part of how we operate in the world, at work, and with brands. Yet many still cannot get it right, nor understand the importance of doing so.
Siloed Enterprise Decisioning Limits AI Innovation
The elephant in the room is that companies and leaders tend to focus on the next big thing, the shiny new object. Over the years, this ranged from the ERP, to CMS, and more recently CRMs and DXPs. Not to mention huge industry shifts: the move to the cloud (and the hope that each platform vendor does so), and now, headless and composable frameworks.
Each of these investment areas came with a promise. A promise to improve efficiency, operations, sales, costs, productivity, and even experience through automation.
The Unstructured Data Problem
However, enterprises remain encumbered with terabytes of structured and unstructured data, created by a history of siloed purchasing behaviors. While well-intentioned, this behavior has made it almost impossible to find or produce anything meaningful. And it resulted in a tension-filled tech environment with various platform vendors viewing the world only through their lens. They miss the bigger picture of addressing the multi-platform, terabyte, and complex realities of enterprises and their need for best-of-breed, agnostic, connected solutions.
And now, once again, enterprises and leaders have their eyes set on the next big thing — ChatGPT, large language models (LLMs), RAG, and Generative AI. Once again, they expect this technology to deliver efficiencies, productivity, savings, and revenue like no other tech has.
Enterprise Connectivity Challenge
But there remains the huge, unwieldy and complex challenge that few had been aware of, or willing to resolve, until now. And that is the need for a unified enterprise fortified by a single source of truth for customers and employees alike. Specifically, the ability to look across platforms, establish interoperability, normalize and standardize data and knowledge, while being able to know what’s most recent, relevant, important to various users and their search intent.
And tackling this siloed enterprise and data challenge is precisely what is needed to make AI projects like GenAI and agentic AI work. To make it relevant, accurate, secure and not hallucinate.
That’s where search relevance comes in. But before we get into search relevance, let’s talk about the evolution of search.
The Evolution and Foundational Nature of Search
For the last 15-20 years, search has gone through a number of growing pains as needs changed. First siloed search for point solution fixes, to federated search that attempted to connect the overall journey. Then came unified search with its goal to provide the best results, across content sources. All of these search approaches were lexical or keyword-based search, which put the onus on searchers instead of on the machine.
Around 2010, mainstream moves to the cloud began. Next came AI recommendation models to enhance search with proactive suggestions on products or content (based on the learnings and the success of users’ online). From there, semantic and conversational search rose as vectorization enabled digital relationship building between concepts.
Hybrid search came out of the need to combine semantic and lexical techniques, to bring together the best of both worlds. Then LLMs, which we know and are so popular today.
And finally, ChatGPT and Generative AI arrived. Now we’re looking straight down the barrel at agentic AI as the next — but certainly not the last — in a long line of evolution.
These advancements and capabilities are important to consider because they demonstrate that, since the early 2000s, there has been a continuum of innovation — tuning, ranking, relevance, analytics, security, AI, reporting, etc. — and it has all been additive, building blocks so to speak based on the core foundational need, capability, and experience that is search.
None of the advanced technologies we know today will work without great search fundamentals. And great search relevance to be exact.
Search Relevance Is Key to Effective AI Grounding
One of the most popular AI grounding techniques today is Retrieval Augmented Generation, or RAG. RAG is important in helping surface contextual information to LLMs. This in turn generates a more accurate answer that end users can trust — and one that doesn’t hallucinate.
But, not all RAG approaches are created equal
Companies differ in how they set it up and which frameworks they use (i.e., LangChain, LlamaIndex, etc.), therefore impacting the quality of the outcome. Also, according to our VP of Machine Learning, Sebastien Paquet, “RAG approaches often reduce the ‘Retrieval’ part to a semantic similarity search in a vector database. But this is not enough as it will not provide the most relevant information to the LLM to generate the best answer.”
Relevant reading: Why Search Engines Outperform Vector Databases for RAG
What’s more, nascent RAG approaches miss on crucial aspects for the enterprise — the full infrastructure needed to manage, standardize, tune, control, and self-optimize search results ranking — at scale and across the siloed platforms aforementioned. Even Gartner highlighted that ‘search augments AI, rather than the reverse’, underscoring the need for strong search relevance.
Relevant reading: Putting the ‘R’ in RAG: How Advanced Data Retrieval Turns GenAI Into Enterprise Ready Experiences
It’s important to talk about relevance a bit more to highlight its criticality
Relevance is the degree to which content, products, or recommendations presented to a user aligns with their needs, preferences, context, behavior, and are easily accessible. It is a crucial factor in providing satisfying, personalized and effective digital experiences.
The additional power of AI is that it not only creates optimum relevance, but it can also balance relevance with driving superior business outcomes (costs, revenue, margins…) at the same time, and in real-time during the point-of-experience.
Must-Have Search Capabilities for Relevance
And great search relevance is achieved through a combination of capabilities that, together, deliver a closed-loop, self-learning system. These include but are not limited to:
- Extensive ranking and filtering to ensure the best content is surfaced to end users
- A hybrid unified index that standardizes and normalizes data from different systems with varying file formats — supporting lexical, vector-based and behavioral techniques
- Manual and auto-tuning options to incorporate domain-specific or business-specific content such as acronyms and company-specific product terms
- Advanced security to ensure users only see what they have access to, across varying systems and different permission settings
- Out-of-the-box connectors and native integrations for popular enterprise platforms, increasing interoperability while augmenting with AI Search & Generative Answering
- Business controls to boost and bury or ‘feature’ select content or products based on business objectives
- Ability to refresh content and data sources at set intervals and easily add new data sources at any time
- Enterprise scalability to ingest and work with millions of documents
- Behavioral AI models that self-learn from user search activity and success which improves the recommendation of content and products, positively reinforcing the entire search and generation system
- Personalization and the ability to leverage real-time and historical behavioral information about a user and their activity online to tailor information
- Analytics and insights on model performance to continuously improve digital experiences
It’s also important to note that RAG and GenAI are just one of many techniques for helping people find, discover, and get direct answers to their needs. Other techniques are still necessary and expected by end users.
Therefore, taking a holistic approach and broad set of capabilities is what will really help comprehensively transform the performance of digital experiences across the enterprise.
An AI Search Platform to Augment Platforms
Since 2005, Coveo has helped more than 700 enterprise customers tackle their toughest data and information management challenges. We started with unified search, analytics, and early-binding security. Now, our platform covers connectivity, integrations, the highest security standards and certifications. It also offers a full suite of machine learning-based solutions including GenAI + 10 purpose-built AI models used across the enterprise for use cases like websites, workplace, service, and commerce.
We have always been early pioneers in search, moved to the cloud early, and have offered AI solutions for more than a decade. Our innovation and commitment to our customers consistently ranks us among the top, best-of-breed providers for Insight Engines (7x), Cognitive Search (6x), Enterprise Search (3x), and Knowledge Discovery Software (2x) as covered by independent analysts Gartner, Forrester, Info-Tech Software Reviews and IDC, respectively.
We believe the worlds of intelligent search, discovery, recommendations, generative answering, conversations, chats, and personalization have all converged. Search is now more important than ever before — as is your vendor selection process and choosing best-of-breed solutions.
But what’s more important is understanding that search, search relevance and an AI Search and Generative Experience platform like Coveo are foundational to creating a connected, intelligent, and dynamic enterprise. One that can successfully and safely leverage advanced technologies like GenAI while giving the business control and generating real ROI.
The reality is that you need an agnostic platform to connect and augment the many siloed systems you have in place today. While also being able to unlock the knowledge and information hidden within each.
You need a holistic solution. You need a closed-loop, fully managed solution. You need an enterprise solution.
Chances are, you need Coveo.