Smarter Search Arrives: Introducing AI Search
🧠 Introducing AI Search in Moustra
We’re thrilled to announce the release of AI Search across Moustra’s Animal, Cage, and Strain list pages — designed to make finding data faster, smarter, and more intuitive than ever before.
No more endless scrolling or guessing filters. With AI Search, you can now search the way you think.
🔍 What Is AI Search?
AI Search replaces traditional keyword filtering with a natural-language-powered search bar that understands complex queries.
You can type things like:
- “Animals born this month and carrying Cre+ allele”
- “Cages with all female mice”
- “Strains with GFP reporter”
Moustra’s AI instantly interprets your intent, scans your database, and returns precise results — even when your wording isn’t exact.
💡 Why It Matters
Traditional search systems rely on rigid filters and exact text matches. That works — but it’s slow, especially when you’re managing hundreds of animals or strains.
AI Search understands:
- Context — knows the difference between “CRE positive” and “Cre+”
- Synonyms — recognizes “DOB” or “birth date” as the same
- Relationships — connects data between animals, cages, and strains
This means fewer clicks, fewer filters, and faster answers.
⚙️ Where You Can Use It
You’ll find the AI Search bar at the top of these pages:
- 🐭 Animal List – instantly find animals by tag, gene, sex, or age
- 🏠 Cage List – locate cages by strain, occupants, or breeding status
- 🧬 Strain List – search by gene name, background, or specific alleles
The search works across columns and relationships, pulling connected data intelligently.
⚡ Powered by Intelligent Understanding
Instead of relying on fixed filters, AI Search uses semantic intelligence — understanding the meaning of your words, not just the text.
It learns from:
- The structure of your data
- Common lab terminology
- Genetic and breeding relationships
So even if you type “CRE carrier moms,” Moustra knows to look for female animals with Cre+ genotypes and offspring linked by pedigree.
Every search is context-aware, providing meaningful matches that feel effortless.
🌟 Designed for Real Lab Workflows
AI Search was built hand-in-hand with researchers to reduce the time spent hunting for information. Whether you’re managing a large breeding colony or reviewing strain data before experiments, AI Search adapts to how you actually work:
- Instant results without manual filters
- Natural phrasing instead of dropdowns
- Continuous improvements as new data is added
This isn’t just a smarter search — it’s a faster path from question to insight.
Real-World Search Scenarios That Used to Take Minutes
To appreciate what AI Search changes, consider how researchers currently find information in a typical colony management system.
Scenario 1: Finding Animals for an Experiment
A researcher needs five female mice, at least 8 weeks old, carrying the Ai14 reporter allele in homozygous state. In a traditional system, this requires setting up multiple filters: sex equals female, age greater than or equal to 56 days, genotype field contains "Ai14," zygosity equals homozygous. Each filter is a separate dropdown or text field. If the researcher types "Ai14" but the database stores it as "Ai14(RCL-tdT)" the filter returns nothing, and they need to troubleshoot the exact string.
With AI Search, the researcher types: "Female mice homozygous for Ai14 older than 8 weeks." Moustra interprets the intent, matches against the actual genotype notation in the database, applies the age calculation, and returns the results. One search bar, one query, accurate results.
Scenario 2: Locating a Specific Cage
A technician receives a veterinary alert about cage B-247 on rack 3. They need to pull up that cage immediately. In a filter-based system, they navigate to the cage list, type the cage number, and hope the numbering format matches. If the cage is stored as "B247" without the hyphen, the exact search fails.
AI Search handles this naturally. Typing "cage B247 rack 3" or "B-247" or even "Baker 247" (if the rack is informally called "Baker") returns the correct cage. The semantic understanding bridges the gap between how people refer to things and how data is stored.
Scenario 3: Checking Strain Availability
A collaborator asks whether your facility has any mice on a C57BL/6J background carrying a LoxP-flanked Tgfbr2 allele. You could scroll through your strain list, or you could set up genotype filters across multiple fields. Or with AI Search, you type: "C57BL/6J strains with floxed Tgfbr2." The search understands that "floxed" means LoxP-flanked, maps it to the correct allele notation in your database, and returns matching strains with their current animal counts.
Scenario 4: Weekly Colony Review
Every Monday morning, the colony manager reviews the state of the colony. What is approaching weaning? Which matings are overdue? Are there any overcrowded cages? This used to require running three separate filtered views and switching between tabs.
With AI Search, the colony manager can run a series of natural language queries directly from the animal or cage list page: "Animals ready to wean this week," "Matings older than 45 days with no litter," "Cages with more than 5 mice." Each query returns results in seconds, and the colony review that used to take 30 minutes finishes in ten.
How AI Search Handles Ambiguity
One of the strengths of AI Search is its ability to handle the inherent ambiguity of laboratory terminology. Research labs develop their own shorthand, abbreviations, and naming conventions that vary from institution to institution.
Genotype notation varies widely. One lab might record a Cre-positive animal as "Cre+," another as "Cre(+)," and a third as "hemizygous Cre." AI Search normalizes these variations so that searching for any of these terms returns the same set of animals. It understands that "Cre carrier" and "Cre positive" and "Cre+" refer to the same biological state.
Date-related queries are interpreted contextually. "Born this month" adjusts automatically based on the current date. "Older than 6 weeks" calculates the correct date threshold. "Born between January and March" translates to the appropriate date range. You do not need to calculate dates manually or enter them in a specific format.
Relationship-aware queries connect data across tables. When you search for "animals in cages with active matings," AI Search joins animal and cage data behind the scenes. Traditional filter systems operate on a single table at a time, requiring you to look up cage information separately and then cross-reference animals. AI Search handles the join automatically.
Performance at Scale
AI Search is designed to perform well even for large colonies. A facility managing 2,000 cages and 8,000 animals needs search that returns results quickly, not a spinning loader while the system processes a complex query.
Moustra's AI Search processes natural language queries and translates them into optimized database operations. The AI interpretation adds only a small overhead compared to traditional filtering. In practice, most searches return results in under two seconds, even for large datasets with complex genotype fields.
For labs that run the same searches repeatedly, the speed advantage compounds. A query that takes 30 seconds to set up with traditional filters but 5 seconds to type in natural language saves 25 seconds per search. Over dozens of searches per day, that adds up to meaningful time returned to research.
AI Search and Cheese AI: How They Work Together
AI Search on list pages and Cheese AI in the chat interface serve complementary purposes. AI Search is optimized for quickly filtering and finding records within a specific list view. Cheese AI is designed for deeper analytical questions that span multiple data types.
A typical workflow might look like this: you use AI Search on the animal list page to find all females carrying a specific genotype. Then you switch to Cheese AI and ask, "Of these animals, which ones would produce the highest probability of double-homozygous offspring if crossed with male M-1892?" AI Search handles the filtering; Cheese AI handles the analysis.
This separation is intentional. When you are scanning a list and need to narrow results quickly, a search bar is faster than opening a chat conversation. When you need the AI to reason about breeding outcomes, compliance data, or colony trends, the chat interface gives Cheese AI the space to provide detailed, contextual answers.
Getting Started with AI Search
AI Search requires no configuration or setup. It is enabled by default for all Moustra users on every list page. Simply click the search bar at the top of the Animal, Cage, or Strain page and start typing in natural language. There is no special syntax to learn, no query builder to navigate, and no training required for your team.
If your first query does not return exactly what you expected, try rephrasing it or adding more detail. AI Search handles a wide range of phrasings for the same underlying question, so "Cre positive females" and "female animals carrying the Cre allele" return the same results. Labs that have been using AI Search for several weeks report that it has become their default method for finding colony data, completely replacing the manual dropdown filter workflows they relied on for years. The time savings compound quickly: what used to require four or five filter selections now takes a single typed sentence.
🧀 Try It Now
AI Search is now live for all users.
Simply log in to your Moustra account and start typing in natural language.
Your colony data has never been this easy to explore.