Modern retail is defined by personalisation, from tailored homepage recommendations to search functions that instantly meet customer needs. But when the experience fails —whether at product discovery, checkout, or post-purchase — retailers risk losing customers immediately.
In an interview with RetailBiz, Jeremy Pell, Area Vice President for Australia and New Zealand at Elastic, discussed how Search AI enhances online journeys, after-sales support, and business performance.
According to Pell, integrating search and artificial intelligence is transforming retail. By analysing meaning, context, and intent, AI-powered search enables retailers to deliver relevant, intuitive, and scalable experiences while equipping employees with the data they need to better support customers.
Driving accuracy and personalisation
Search functions are the primary gateway to the digital world and can influence every purchase decision. Traditional keyword-based searches often failed to capture meaning or intent.
Pell explained that Elastic’s technology delivers accurate, personalised results through the Elasticsearch Platform, which combines the precision of keyword search with the intelligence of semantic and vector search.
“Semantic search allows the platform to understand the meaning behind words, while vector search uses mathematical representations of data to find similarities even when the exact term isn’t used. This means a query like ‘women’s spring fashion 2025’ won’t just return literal matches; it can also bring back results like ‘ruffled skirt’ or ‘maxi dress’ because the system recognises the context and underlying intent.”
Elastic unifies keywords, semantics, and vector search to align results with seasonal trends, purchase histories, and customer patterns. Through multimodal search across text, images, video, and audio, the platform enables experiences like image-based product matching by size and budget.
Keeping systems stable during peaks
And as retailers face immense pressure during high-traffic events like Black Friday. Pell said Elastic Cloud Serverless prevents crashes by automatically scaling resources based on demand, eliminating manual capacity planning.
“Elastic is built to handle massive traffic surges like Black Friday, and with Elastic Cloud Serverless, scaling has never been easier. High-traffic events like Black Friday are not just about more customers; they produce more data, more transactions, and more pressure on systems.”
Elastic aggregates logs, traces, and customer data into a unified view, maintaining rapid search performance and responsive websites for millions of users.
“Elastic Observability empowers IT teams in implementing AIOps, the use of artificial intelligence (AI), machine learning (ML), and big data analytics to automate and improve IT operations. Using AIOps, IT teams will no longer need to scrub through terabytes of logs and instead use AI to analyse logs to estimate the impact or ‘blast radius’ of a fault or identify problems in their application or IT infrastructure in seconds, not minutes.”
By anticipating infrastructure needs and preventing downtime, Pell said that Elastic ensures a seamless end-to-end customer experience from search to checkout.
“Every minute counts, and disruptions will result in missed purchases and lost customers. Elastic Observability helps with operational resilience to create a frictionless retail experience for customers.”
Strengthening cybersecurity
Moreover, with retailers holding vast amounts of customer data, cybersecurity remains a major concern. Pell said Elastic’s Search AI platform helps IT teams detect, investigate, and respond to threats efficiently.
“Elastic Security is built on the Search AI platform, and its open architecture brings unified analytics and AI to data, combined with the latest threat intelligence, enabling detection, investigation, and response at scale without the need to move or duplicate data.”
Through features like Attack Discovery, Elastic Security uses AI and Retrieval Augmented Generation (RAG)-based context to identify and correlate threats.
“Elastic’s AI SOC Engine (EASE) also leverages AI to bring context-aware detection and triage into existing Security Information and Event Management (SIEM) and Endpoint Detection and Response (EDR) tools.”
Empowering employees with insights
Pell believes that empowering employees with instant data access drives productivity at every level.
“The Elasticsearch Platform allows retailers to consolidate data from all of their internal systems such that customer service teams or store staff can ask questions in natural language and receive relevant answers instantly.”
“For retail staff, that means insights aren’t buried in complex systems or siloed teams. A customer service agent can type a natural question, like ‘show me delivery delays in Sydney this week,’ and instantly get an answer they can act on.”
By enforcing permission-based access, Elastic ensures employees can securely retrieve relevant data — such as invoices or receipts — to deliver faster, more personalised service without relying on data teams.
Local proof of Search AI success
Pell cited The Warehouse Group as an example of Search AI’s tangible impact on customer experience and fraud prevention.
“Previously, The Warehouse Group did not actively encourage returns with a receipt, which impacted customer satisfaction due to the extended time it took to process returns. Some customers also attempted to return items that they never purchased at The Warehouse… This was a fraudulent opportunity that The Warehouse wanted to close.”
Elastic’s Search AI allowed the retailer to verify returns and purchases instantly across 1,500 POS systems and 200 service desks, analysing three billion transactions.
“For example, if a customer comes in to return a toaster (with or without their original receipt), an employee can easily search for the toaster, along with any items purchased alongside it… and confirm that it has not been returned already.”
What sets Elastic apart
According to Pell, Elastic differentiates itself by merging search technology and AI to enhance enterprise information retrieval—much like how lakehouses combine data lakes and warehouses.
“It uniquely enables organisations to capitalise on the exponential growth of underutilised, unstructured data. Search AI ensures that the most relevant data is found efficiently to generate answers based on a user’s original question.”
“At Elastic, we see huge opportunities to support businesses by building a bridge between company data and generative AI applications… Beyond simple keyword searches, Elastic empowers businesses and their users with search capabilities that return results from data ranging from images and audio to documents based on the meaning of the query, not just keywords.”
Elastic’s balance of speed, trust, and scalability has made it a trusted partner for over half of the Fortune 500.
Expanding beyond retail
Pell said Search AI’s potential extends well beyond retail.
“While retail is an obvious example, the same principles apply in other industries. In healthcare, Search AI can help clinicians instantly retrieve patient records or the latest medical insights. In government, it can simplify access to citizen services… In financial services, it is already being used to detect fraud in real time.”
Elastic’s ability to index and analyse massive data volumes helps detect fraud and cyberattacks faster, with fewer false positives.
“This is where Elastic is unique. By combining the speed and precision of search with the intelligence of AI, our Search platform enables organisations to index, search, and analyse vast volumes of data at petabyte scale and millions of events per second.”
The next wave of innovation
Looking ahead, Pell highlighted emerging Search AI innovations such as Agentic AI—proactive assistants capable of handling complex customer tasks.
“Instead of manually searching and filtering, customers will be able to delegate complex tasks to the AI agent… This fundamentally changes the customer journey from active searching to passive, goal-oriented delegation.”
He also noted the rise of context engineering, which uses rich contextual data—like purchase history, location, or weather—to generate more accurate and anticipatory AI responses.
“It’s the practice of strategically feeding the AI with rich contextual data… For example, it could proactively recommend a raincoat to a customer in an area expecting rain.”
By combining Agentic AI and context engineering, retailers can create intuitive, data-driven shopping experiences that deepen customer engagement and loyalty.