This is where it started
“Product conversion lives in analytics tools while AI execution quality lives in debugging tools, making it hard to judge one change across both outcomes and quality.”observed → Started 2026.07.20
— So we built
Ops Console
LABopsweb service
A unified operations platform connecting product outcomes with AI execution quality.
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Developer evidence
What I owned in this project
- Period
- 2026.07 – Operating now
- Team
- Solo developer
- Contribution
- Planning · design · development · deployment · operations, 100%
- Owned scope
- Uses separate boundaries for read-only connectors, privacy-safe normalization, deterministic aggregation, RAG reporting, and numeric-claim verification.
Introduction
Ops Console normalizes PostHog and GA4 product events with LangSmith execution and quality signals across projects. Operators can ask natural-language questions over the same evidence and generate analyses and reports with verifiable numeric sources.
User flow
- 01
Connect a project and its data sources, then verify collection scope and event contracts.
- 02
Normalize product events and AI runs or feedback onto a shared identifier for comparison.
- 03
Ask questions in natural language and review analyses and reports linked to numeric evidence.
Highlights
- Joins PostHog and GA4 product behavior with LangSmith AI execution signals through a shared operation ID.
- Compares project KPIs with AI quality, cost, and latency in one view.
- Generates natural-language analysis and reports grounded in verified aggregates and project knowledge.