Mastering Kaiya conversational AI

The full spectrum of query capabilities that Kaiya supports

Kaiya AI Agents are a fully orchestrated analytics engine, where you can use natural language to automate everything from simple metrics to advanced diagnostics.

With Kaiya AI agents, you can:

• Move from simple metrics to root cause insights in a single flow.

• Leverage AI orchestration to avoid manual report building, SQL writing, or dashboard tweaking.

• Handle complex logic—filters, groupings, comparisons—without needing technical expertise.

• Trust Kaiya to guide you, even when queries aren’t perfect.

1. Live data access vs. Optimized historical queries

Kaiya dynamically selects data sources based on your query's time context.

• Live data queries When you ask for recent periods ("Show me TRx for this month"), the Data Prep Agent pulls from live, constantly updated datasets. This ensures you're making decisions on the most current information.

• Historical queries If you're analyzing past periods ("Show TRx for 2022"), Kaiya automatically optimizes performance by querying cached datasets or archives, reducing load times without compromising accuracy.

• Smart de-duplication For domains like pharma sales where duplicate transactions exist, queries like "Show TRx for Ioxalin in April 2024" trigger deduplication logic within the Data Prep Agent—no manual cleaning required.

2. Conversational context: Follow-up intelligence

Unlike static BI tools, Kaiya retains session context across your queries and adapts query structure without requiring you to restate parameters. If you ask, "Show TRx for March 2024", and follow up with "How about last month?", Kaiya understands that "last month" refers to February 2024, applies previous filters (e.g., product, region), and re-runs the query.

Other follow-up capabilities:

• "What about by product?" Kaiya understands that you now want to see the previous metric broken down by product. It automatically restructures the query to group the results by the "Product" dimension.

• "Show it weekly instead" Kaiya switches your data view to show weekly trends, keeping your previous selections (like product, region, or date range) intact.

3. Advanced time handling

Kaiya’s date/time parsing engine allows you to express time filters naturally:

• "Show TRx after November 2023" Kaiya understands that you're asking for all TRx (Total Prescriptions) and applies a date filter to include only records starting from December 1, 2023, onward.

• "Monthly TRx for PillABC" Kaiya organizes the TRx numbers for PillABC into a month-by-month breakdown, so you can easily see how prescriptions change over time.

• "For 'March 2024'" When you use quotes like "March 2024", Kaiya treats it as a specific, fixed time period.

Kaiya also intuitively understands relative timeframes: "Last quarter", "Past 6 months", "Year-to-date"

4. Dynamic filtering across values, lists, and substrings

Kaiya automatically applies the right filters based on how you describe them—whether it's a single value, multiple options, or partial matches.

• Single filters: "Show TRx for Virginia" → Applies exact match filters.

• Multi-value filters: "Show TRx for Virginia and California" → Builds compound filters with proper logical operators.

• Substring matching: "Indication contains OTH" → Translates to LIKE/CONTAINS logic in the backend.

• Logical combinations: "TRx for dermatology in NY OR NJ" → Correctly applies AND/OR logic, respecting precedence.

5. Group by and Aggregation

You can organize and analyze complex datasets by simply describing how you want to break down your data. Kaiya supports SUM, AVG, MIN, MAX, Top N / Bottom N, and nested groupings without explicit instructions.

• "Show TRx by region and product" Kaiya automatically applies multi-level grouping, summarizing Total Prescriptions first by region, then by product within each region—exactly like you'd build a nested pivot table, but without manual setup.

• "TRx by all ecosystems" Kaiya interprets the word "all" intelligently, expanding your request to group data across every relevant dimension (e.g., region, segment, product category), without needing you to list them explicitly.

• "Top 5 HCPs by Units" This triggers both an aggregation (summing Units) and a ranking function, returning the highest performers.

6. Comparisons made effortless

Kaiya handles aligning datasets, managing time frames, visualizing side-by-side comparisons. With simple phrasing, you trigger complex comparative analysis:

• "Compare NRx between Decile 7-8 vs 9-10" Kaiya understands you're asking for a side-by-side comparison of two HCP segments (Deciles 7-8 vs. 9-10), and it automatically filters, groups to calculate the differences between these segments

• "TRx in Q1 2024 vs Q1 2023" This triggers a period-over-period comparison, where Kaiya calculates both the absolute change and the percentage change in Total Prescriptions between the two quarters.

• "Marketshare of ProductK vs ProductL in CA" Here, Kaiya recognizes you're asking for a product vs. product comparison within a specific region. It retrieves the market share data for both products and presents them in a comparative format.

7. Growth and trend calculations

You don’t need to specify formulas for growth. Kaiya can distinguish between point-in-time comparisons, continuous growth trends, event-based deltas ("before and after campaign in Oct 2023").

• "Show TRx growth over 6 months" Kaiya understands you're asking for how Total Prescriptions have changed over the past 6 months. and automatically calculates the percentage growth (or decline) month-over-month.

• "NRx growth in West vs East" Kaiya detects that you're looking for a regional comparison of growth. It gives side-by-side growth metrics by segment (in this case, West and East regions), highlighting which region is growing faster or declining.

• "Monthly trend of TRx for Product X" Recognizing the time-based trend analysis, Kaiya plots Total Prescriptions for Product X across each month, showing how Product X's prescriptions have moved month by month—perfect for spotting peaks, drops, or seasonal patterns.

8. Insight-driven questions ("Why" and "Compare")

When you ask:

• "Why did TRx drop in Q2 2024?"

• "Compare TRx between Q1 2023 and Q1 2024"

Kaiya triggers the Insights Agent to do more than just show numbers. It identifies key drivers, breaks down variances, highlights top contributing factors, and presents both visuals and clear explanations—automating what would normally be manual analysis. You get immediate diagnostic insights without extra effort.

9. Handling complex filtering logic

Kaiya translates your natural language into precise backend logic, without requiring you to use parentheses or Boolean operators.

• "TRx for Product N in NY where HCP Decile is 9" Kaiya applies nested filtering, combining multiple conditions—filtering by product, location, and a specific HCP segment—all in one query.

• "TRx for dermatology in NY OR NJ" Kaiya correctly interprets and handles the logical operators (in this case, "OR").

Kaiya handles errors gracefully by providing clear, actionable feedback—whether it's an invalid dimension, unknown filter value, missing metric, or backend issue—so you’re guided to fix the input without confusion or dead ends. It never fails silently, ensuring smooth, transparent interactions.