type: workstream slug: inspect-profiler name: inspect profiler description: Database profiling, semantic typing, and context generation for analysts and AI. owner: sr-engineer-architect status: active milestones: m0-prototype: |- A runnable prototype path exists for warehouse profiling, semantic inference, and analyst-facing data context surfaces, with concrete artifacts that prove the flow works end-to-end in the current codebase. Core assumptions are documented, known constraints are explicit, and the team can explain what is real versus mocked without ambiguity. m1-ft-analytics-analyst-pilot: |- Internal analysts can execute at least one weekly real workflow that depends on warehouse profiling, semantic inference, and analyst-facing data context surfaces in the 5T Analytics environment, without bespoke engineering intervention for every run. Instrumentation and feedback capture are in place so failures, friction points, and adoption gaps are visible and triaged with owners. m2-internal-adoption-design-partners: |- warehouse profiling, semantic inference, and analyst-facing data context surfaces is hardened enough for regular use by multiple internal teams and initial design partners, with a predictable response loop for issues and requests. Quality expectations are documented, and prioritized improvements from real usage are actively incorporated into delivery. m3-public-launch: |- Launch scope for warehouse profiling, semantic inference, and analyst-facing data context surfaces is complete, externally explainable, and supportable: user-facing behavior is stable, documentation is publishable, and operational ownership is explicit. Remaining gaps are non-blocking, risk-assessed, and tracked as post-launch follow-up rather than unresolved launch debt. m4-v1-0-launch: |- Post-launch stabilization is complete for warehouse profiling, semantic inference, and analyst-facing data context surfaces: recurring incidents are reduced, support burden is lower, and quality gates are enforced consistently before release. The team has a repeatable operating model for maintenance, regression prevention, and measured reliability improvements. m5-v1-2-launch: |- v1.2 delivers meaningful depth improvements in warehouse profiling, semantic inference, and analyst-facing data context surfaces based on observed usage and retention signals, not just roadmap intent. Enhancements improve real customer outcomes, and release readiness is demonstrated through metrics, regression coverage, and clear migration guidance where relevant. mx-far-future-ideas: |- Long-horizon opportunities for warehouse profiling, semantic inference, and analyst-facing data context surfaces are captured as concrete hypotheses with user impact, prerequisites, and evaluation criteria. Ideas are ranked by strategic value and feasibility so future investment decisions can be made quickly with less rediscovery.
Database profiling, semantic typing, and context generation for analysts and AI. The inspect module connects to a user's warehouse, profiles tables/columns, detects semantic types (currency, email, timestamp, etc.), classifies data quality, and produces structured context that feeds into dashboard generation and MCP tools. This is the "understand the data" layer — it turns raw schema into rich metadata that analysts and AI agents use to ask better questions and build better dashboards. Adjacent to context-catalog-nimble (which defines the context architecture and Nimble methodology) and mcp-analyst-agent (which consumes inspect output as tool context).
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