insights
We often associate analytics with just numbers such as how many visits, how long users stay, what they click. But what if we could understand the emotional intent behind those actions?
To move towards that goal, we embedded a layer of intelligence in Smart Search that learns from patterns in user behavior over time. By applying clustering and classification techniques, the system begins to recognize recurring search themes, categorize similar queries, and even predict what users are likely to type next based on community-wide behavior.
What this creates is a subtle yet powerful loop of personalization. When someone begins a search, the system can autocomplete their thoughts or suggest adjacent topics. That way, we are gently guiding them, just as a helpful colleague might do during a brainstorming session.
But the benefits also extend to the people managing the platform. Administrators can view search trends to understand what truly resonates with users, what’s being searched but not found, and what opportunities exist to fill those gaps. Imagine discovering that a large number of users are searching for “remote internship programs” even though your site doesn’t have that exact content. That’s not just a missed click; that’s a signal, an unmet need.
In this way, Smart Search becomes a quiet researcher that is actively gathering and surfacing insights that help teams create more intentional content strategies.
What we’re building here isn’t just a better tool but a better relationship between people and technology.
Take education platforms for example, we imagine Smart Search empower students to ask complex questions and receive clear, layered explanations without having to navigate through dozens of links and references.
On corporate websites, Smart Search can help employees navigate a large depository of documentation and forms in seconds instead of hours. When AI is used right, it builds trust. People feel seen, understood, and respected. That, to me, is the heart of good design and good AI systems that put human at the center, not efficiency and processes.
Of course, none of this came easy. We encountered plenty of friction along the way — especially around messy content data, long LLM processing times, and the evolving nature of user expectations.
These give us the opportunity to ask: How can we make this simpler, clearer and more helpful?
We fine-tuned our tagging engine to ignore irrelevant noise and focus on what matters. We continue to optimize our solution architecture to allow phased delivery without compromising user experience. And we build feedback loops into the system, so it gets smarter with every search.
Each of these decisions was driven by empathy. We weren’t just solving for efficiency but solving for experience.
The road is a long one. Our platform development roadmap is full of exciting possibilities: voice search, image-based search, conversational follow-ups, federated knowledge systems across platforms.
But as much as we’re innovating, our focus remains simple: Make search feel more like conversation, and less like decoding a machine. Because in the end, technology should not make people feel smaller but to their ability to find, learn, and connect.
Josephine Toh is a dynamic force at the intersection of digital strategy, product innovation, and artificial intelligence in Malaysia. Currently leading initiatives at XIMNET, a digital consultancy known for its forward-thinking approach, she has been instrumental in developing AI-powered solutions such as XTOPIA, a SaaS platform.
At XTOPIA, user-centric business solutions like Smart Search, AI Forms, and intelligent content engines to serve organizations across Asia.
Her commitment to responsible innovation is grounded in the belief that technology should be “real, good, and human.” Whether she’s facilitating workshops to help teams reinvent their workflows using AI, speaking to boards about AI readiness, or shaping the future of AI-driven platforms in Malaysia, Jo is poised to be a catalyst in Malaysia’s growing AI ecosystem. #WomenInAI
