Analytics & LLM Insights. Data Your Engineering Team Can Act On. Numbers Your Leadership Can Trust.
Track test execution trends, defect rates, team productivity, and AI model token usage across every project and sprint. Turn QA data into release confidence and ROI reporting.
Trusted by QA leads and engineering managers who run data-driven releases
Execution trends, defect rates, and coverage metrics updated as tests run — not at report time
Visibility across test quality, team productivity, AI model usage, and cost in a single dashboard
Historical trend data across sprints surfaces patterns before they become incidents
Most teams only look at quality data after something goes wrong
A release-day dashboard tells you what already happened. WalnutAI's analytics surface trends across sprints — rising defect rates, slipping coverage, flaky tests accumulating, AI model costs creeping up — early enough to act. QA leads get the operational data they need for sprint retrospectives. Engineering managers get the executive-ready metrics they need for release confidence and ROI conversations.
Track test execution trends across every sprint and project
Pass rates, failure distributions, execution velocity, blocked test rates, and defect discovery trends are tracked continuously across every project and sprint. Patterns that indicate quality risk become visible weeks before they would surface as production incidents.
Spot and eliminate flaky tests before they erode confidence
Flaky tests — tests that pass and fail intermittently without code changes — silently undermine release confidence and waste QA time. WalnutAI’s analytics identify flaky tests automatically so they can be fixed or quarantined before they distort your execution results.
Monitor AI model cost and token usage per project
Every AI operation in WalnutAI — story generation, gap analysis, test case creation, code generation — is tracked by model, token count, and estimated cost per project. Engineering managers can see exactly what AI is costing, control spending limits, and compare cost-per-output across different model configurations.
Executive-ready reports without manual compilation
Generate release readiness reports, sprint quality summaries, and AI ROI dashboards in one click — formatted for leadership review, not just QA internal use. No manual data gathering, no spreadsheet assembly, no time spent translating test data into business language.
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See how WalnutAI connects requirements, code, testing, and deployment into one intelligent workflow.