Telemetry Insights

Value Page

Tursio provides a value page with metrics demonstrating how the platform speeds up adoption, delivers measurable time savings, and translates efficiency into tangible business value. This dashboard allows users to adjust the date range (last 7 days, last 30 days, or a custom period), providing flexibility in analyzing performance across different reporting windows.

Adoption Rate. The total number of queries processed by Tursio within the selected time period. With 145 queries, adoption has increased significantly (+98) compared to the prior cycle, reflecting stronger user engagement and reliance on the platform for day-to-day insights.

Value Added. The cumulative hours saved by automating query authoring. On average, writing a query manually takes about 10 minutes, whereas with Tursio this reduces to under 3 minutes. This efficiency has resulted in 16.85 hours saved — an improvement of 11.40 hours over the previous period.

Estimated Business Impact. Time savings converted into financial value by applying an industry benchmark of $50 per analyst hour. In this cycle, Tursio delivered an estimated $842.39 in productivity gains, representing a $570.09 increase from the last reporting period.

Trends. The supporting charts visualize adoption and time savings across the reporting period:

• Adoption Rate: Displays the number of queries processed each day. Peaks in activity highlight the days when Tursio was most heavily used, reinforcing how adoption drives engagement.
• Value Added: Translates activity into hours saved per day. Taller bars indicate higher time savings, and the direct correlation with query volume demonstrates the compounding effect of increased adoption.

Each chart is interactive, offering options to zoom in, zoom out, pan, box select, lasso select, auto-scale, and reset axes — enabling analysts to closely examine trends and focus on specific time ranges. Graphs can also be downloaded for offline review or reporting.

The combination of metrics and trend charts underscores a clear story: as adoption grows, productivity gains and business impact scale proportionally, delivering measurable ROI for enterprises.

Metrics Page

The metrics dashboard provides visibility into the system's operational efficiency, accuracy, and model deployment trends.

Users can select a date range (last 7 days, last 30 days, or a custom period) for flexible performance analysis across different reporting windows.

Performance. The average time taken to process a request end to end. In this period, the system achieved an average latency of 2.8 seconds — a 0.7-second improvement from the previous cycle, reflecting faster query resolution and improved responsiveness.

Accuracy. The percentage of queries that compiled successfully. With a 100% success rate, the system consistently ensured reliable execution of user requests, reinforcing trust in the quality of responses.

Models. The number of small models most recently deployed. A total of 739 models are active, with a slight decrease of 5 compared to the prior period. This metric helps track ongoing model updates and system scalability.

Trends. The charts provide deeper visibility into system performance and workload:

• Latency: Displays the average processing time per day. Variations highlight how workload or optimization measures impact speed. The overall trend shows stable performance within acceptable ranges.
• Workload: Shows the volume of queries handled daily, segmented by type. Spikes indicate peak demand periods, while consistent response times during high workloads reflect the platform's scalability.

Each chart is interactive, offering options to zoom in, zoom out, pan, box select, lasso select, auto-scale, and reset axes — enabling analysts to closely examine trends and focus on specific time ranges. Graphs can also be downloaded for offline review or reporting.

Query History

The Query History feature in the Insights tab provides full visibility into previously executed queries, enabling accurate tracking, dataset-level filtering, feedback monitoring, and seamless navigation back to search workflows.

By default, Query History displays all queries executed within the last 30 days, including the timestamp, user, search mode, dataset, query text, and associated feedback (positive, negative, or none). The view is dataset-aware — when a user switches datasets, the history automatically refreshes to show only queries relevant to the selected dataset, with no stale data carried over.

Users can refine results using a date range filter, which reloads data when changes are applied and strictly limits results to the selected timeframe. Each query entry is interactive: selecting a query redirects the user to Search Home, auto-populates the search field, and automatically executes the query to display results.

For Research mode queries on non-Cassandra datasets, an Edit Data Model option is also available. Selecting this option redirects the user to Search Home, auto-runs the query, scrolls to the Data Model section, and opens it directly in edit mode.

The system also supports structured feedback management. Negative feedback entries display alert indicators only when valid and not previously ignored. Users can ignore feedback individually or in bulk within a selected date range. Once ignored, alert indicators are removed and the interface refreshes to reflect the updated state.

Together, these capabilities ensure reliable query tracking, precise filtering, structured feedback governance, and tight integration with search and data model workflows — supporting operational transparency, streamlined analysis, and scalable enterprise usage.