qaAutomationStatus is
Automated · categorised from the
Function / Component fields on the ADO test cases
| Project | PRs Merged | PR Cycle P80 | Active WIP | Throughput | Cycle Time | Rework Rate | Builds | Avg Build | Build Fail % |
|---|
| Project | Reliability | Security | Maintainability | Bugs | Vulnerabilities | Hotspots | Coverage | Lines of Code |
|---|
| Project | Critical | High | Medium | Low | Total |
|---|
| Work Item | Type | State | Assigned To | Original Est. | Remaining | % Done | Task Sum | Project |
|---|
Customer personal data is not available for query.
Customer names, email addresses, phone numbers, dates of birth, Social Security and
licence numbers, and bank or card numbers are blocked — a query naming
one is refused outright, and those fields are stripped from results even on
SELECT *.
Agencies and agents are not customers, so their identity is available —
agency name, business/entity name, location, primary contact, agent name, NPN and FEIN.
That is deliberate: an answer that can only name a producer as
agent 6816 is unusable, and because the same number is a valid id in more
than one table, an unresolved code can point at the wrong business entirely. Agent
contact details (email, phone) stay blocked — identity yes, contact no.
Safe to ask about: policies, quotes, premiums, claims, coverage, deductibles, rating and eligibility, product and contract configuration, counts and trends by state, city, ZIP, county or geography, plus release history, code, pipelines, Azure DevOps work items and application telemetry. Addresses are the one exception and may be queried — that is how you scope a question to a region. Records are identified by policy or quote ID, never by person. Orion180 business use only: personal requests and anything inappropriate for a workplace tool will be declined.
This is an AI assessment of our data, not a system of record. Answers are generated by a model reading source, Azure DevOps, telemetry and the database on the fly. It can miss data, pick the wrong scope or date range, or state a plausible figure with more confidence than the underlying data supports. Always verify for completeness and accuracy before acting on or sharing a result — check the queries and sources shown with the answer against the system it came from, especially for anything going to a customer, a regulator, or a decision.