Improving search and discovery is critical to increasing domain sell-through rates, but it’s a complex challenge. A strong brand name doesn’t always contain an exact keyword—it can be a conceptual blend, an abstract idea, or an evocative term that captures an industry or feeling. At Atom.com, we’ve been working to refine this process using AI, developing a deep understanding of word relationships and search intent. Search 2.0 is a step forward in that effort, bringing more precision and relevance to domain discovery.
This release is currently in Beta, and we will continue refining it based on real-world usage and feedback.
Adapting to Different Search Behaviors
Buyers approach search in different ways—some start broadly, while others seek highly specific terms. Search 2.0 dynamically adapts to both approaches:
- Concept-Based Searches: A buyer searching for “dating” may not be looking for the word itself in a domain but rather for words that suggest connection, chemistry, or attraction—like “Spark,” “Heart,” or “Crush”. The new search tech better recognizes these relationships and presents the most relevant results.
- Exact Keyword Matches: Some buyers want precise keyword matches with no deviations. If someone searches for “Pay,” they may only want domains that contain “Pay”—not “Wallet” or “Swipe.” Search 2.0 helps buyers achieve this level of precision with one click.
Better Precision: Must Include & Must Exclude Filters
One of the most impactful upgrades in Search 2.0 is the introduction of Must Include and Must Exclude filters, giving buyers greater control over search results. These tools allow users to fine-tune their searches and quickly zero in on the most relevant domains, reducing noise and irrelevant listings.
- Must Include: Ensures that specified words appear in all search results.
- Must Exclude: Filters out domains containing unwanted words.
Why This Matters
Previously, search was a linear experience—buyers would enter a single keyword, review the results, and then start a new search with another keyword. This limited exposure to domains that could have been relevant but were buried under broader search terms. Now, with Must Include and Must Exclude filters, buyers can include and exclude multiple keywords at once, refining their results in a more targeted and dynamic way.
This means domains that may have been overlooked in previous searches now have a much better opportunity to be seen.
Real-World Examples
Fashion & Apparel: A buyer searching for “clothing” might see a variety of results with words like “Glam,” “Style,” and “Thread”. If they prefer sleek, modern branding, they can require “Glam” and “Style” while excluding “Thread,” removing results that don’t fit their vision.
Tech & AI Startups: Someone looking for AI-related domains may search for “Neural” and get results containing “Bot,” “Smart,” and “Automate.” If they want a cutting-edge feel, they could include “Neural” and exclude “Bot” to avoid domains that sound too robotic.
Food & Beverage: If a buyer is looking for domains related to “Coffee,” but they keep seeing names with “Brew” and “Beans,” they can tweak their search to include “Barista” and exclude “Beans” to better match their brand identity.
Self-Improving Feedback Loop Using AI Agents
Previously, seller-defined categorization played a major role in domain rankings, but it sometimes resulted in misplaced or irrelevant results due to incorrect root word assignments. While categorization remains important, we have now trained AI agents to refine and enhance these rankings continuously.
These AI agents continuously simulate buyer searches, testing various keywords and critically evaluating domain results for relevance.
As a result, the most relevant domains gradually rise to the top, while less relevant ones are pushed down.
Additionally, we incorporate real buyer engagement signals—if certain domains perform well for specific keywords, the system adapts, reinforcing their rankings through a continuous feedback loop.
This dynamic, self-improving process ensures that search quality evolves over time, leading to more precise and relevant results.
Managing Exact SLD Matches Without Overcrowding
If a search term exactly matches a domain’s SLD—for instance, “Chef” in Chef.com—it will appear at the top of results. However, Search 2.0 balances results to ensure diversity, as buyers have different intent and preferences.
Additionally, the system prevents results from being overwhelmed by too many less popular extensions. Searching for “Merge” will likely highlight Merge.ai, Merge.io, but will not flood first page results with every possible variation of less popular extensions.
Universal Search: More Visibility for All Listings
In previous iterations, Plus and Standard listings were relegated to separate sections and often went unnoticed. Search 2.0 integrates them into the main results in a more natural way, increasing their visibility while keeping Premium listings prioritized.
- Premium listings (curated) appear first.
- Plus listings follow.
- Standard listings come next, ensuring they remain visible to interested buyers.
Soon, we’ll also introduce a visual indicator to distinguish curated collections from uncurated ones, making it clearer where each listing stands.
Looking Ahead
Search 2.0 represents a significant step forward, but it’s still evolving. While this update improves precision, relevance, and filtering, we consider search an ongoing project—one that will continue to improve with refinements, deeper AI integration, and ongoing enhancements.
News Source:Darpan Munjal，This article does not represent our position.
