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MVP concept

Regulations change.
The context should keep up.

Kobalt Labs helps companies understand how current regulations apply to their documents and circumstances. This concept explores a companion data layer: tracking how those regulations change and anticipating what could happen next.

The foundation is accurate, traceable regulatory data. This monitor links official publications into a history, separates observed changes from AI forecasts, and keeps the evidence behind each assessment.

What this could become

This is an early MVP, nowhere near a finished product. It demonstrates the data and forecasting workflow. Company-specific impact analysis, alerts, and validated predictive accuracy are future work.

Explore the architecture
Regulatory Monitor / Architecture
Back to monitor

Current data model

Two active Supabase tables, saved together through save_activity_case. Row-level security restricts access to the server. Eight retained legacy tables are outside this MVP's current workflow.

100%

  1. activity_records

    Latest case for a selected publication. Stores its publication date, checked_at timestamp, and complete case payload as JSONB.

    PK document_number

  2. activity_assessments

    Immutable assessment snapshots, including research, validation findings, and model reviews. A content fingerprint deduplicates identical cases. Each assessment belongs to one selected publication.

    PK fingerprint · FK document_number

  3. Source fields in the payload

    Selected publication, linked history, source URLs, linkage method, completeness, limitations, official excerpt, and current status.

    JSONB fields, not a separate table

  4. Forecast fields in the payload

    Target event, issue date, window end, reasons, counterargument, and watch signals. AI metadata retains tool actions, source excerpts, comparisons, review, model, prompt, and input hash.

    JSONB fields, not a separate table

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