Quality and performance improvements

Problem

By v1.2, real-world usage will have exposed performance bottlenecks and quality issues in the platform integration layer: slow Cloud Run cold starts affecting dashboard load times, Stripe webhook processing delays causing stale subscription state, or warehouse connectivity timeouts under concurrent query load. These issues degrade user experience but are not addressed by feature work. Without dedicated quality and performance investment — with measurable targets tied to user-facing outcomes like page load p95, billing sync latency, and query error rates — the platform accumulates technical debt that erodes the product's reliability reputation.

Context

• Once deployment, billing, connectivity, and production launch integration is in regular use, quality and performance work needs to target the actual slow, flaky, or costly paths rather than generic optimization ideas.
• The right scope here is evidence-driven: identify bottlenecks, remove the highest-friction issues, and make sure the fixes are measurable and regression-resistant.
• Expected touchpoints include deployment automation, environment/runbook docs, billing/integration code, and ops checks, telemetry or QA evidence, and any heavy workflows where users are paying the cost today.

Possible Solutions

• A - Tune isolated hotspots as they are reported: useful for emergencies, but it rarely produces a coherent quality/performance program.
• B - Recommended: prioritize measurable bottlenecks and quality gaps: couple performance work with correctness and UX validation so improvements are both faster and safer.
• C - Rewrite broad subsystems for theoretical speedups: tempting, but usually too risky and poorly grounded for this milestone.

Plan

1. Identify the biggest quality and performance pain points in deployment, billing, connectivity, and production launch integration using real usage data, QA findings, and support feedback.
2. Choose a small set of improvements with clear before/after measures and explicit user-facing benefit.
3. Implement the fixes together with regression checks, docs, or operator notes wherever the change affects behavior or expectations.
4. Review the measured outcome and turn any remaining hotspots into sequenced follow-up tasks instead of leaving them as vague future work.

Implementation Progress

Review Feedback

• [ ] Review cleared