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Useful dashboards need an operating contract. Each report should declare who reads it, when it refreshes, which decision it drives, which marts it reads, how freshness is proven, who owns it, and what gate must pass before it replaces the old workflow.
AI workplace dashboards get messy when teams start with charts. The safer pattern is to start with the report catalog: audience, cadence, decision, source marts, status, and cutover gate first; visuals and drilldowns second.
This keeps the dashboard tied to public-source or synthetic evidence patterns, precomputed hot marts, reviewed labels, materialized retrieval outputs, and explicit ownership. If a report cannot name the decision it supports, it should stay in draft.
Every report has audience, cadence, decision, required marts, status, and cutover gate. Add visuals, drilldowns, freshness source, API endpoint, and owner before the report becomes live.
These fields are intentionally practical. They turn a dashboard idea into a report contract a team can test, own, refresh, and retire.
Use this as the minimum report declaration. It works for a dashboard, a scheduled report, a local notebook export, or a lightweight API-backed view.
Name of the dashboard or report
Who reads it and what detail level they need
Refresh schedule and review schedule
The specific choice, escalation, or operating action this artifact supports
The first visible chart, queue, table, or summary
Evidence views that read bounded marts or accepted retrieval bundles
DuckDB, SQLite, Parquet, JSON, or API-ready hot mart names
Snapshot manifest, row counts, input hashes, prompt versions, and stale reasons
The route that serves materialized report data without live AI work
Person accountable for freshness, correctness, and usage
draft, ready, live, or retired
The proof required before the report replaces an old workflow
These examples use generalized workplace patterns: workflow audits, stakeholder follow-through, decision visibility, and label quality. Replace the names with your own public-source or synthetic evidence sources.
Audience: Manager and IC reviewing one workflow improvement lane.
Cadence: Weekly refresh after the evidence batch; read during the weekly operating review.
Decision it drives: Choose which workflow bottleneck should be fixed, documented, delegated, or escalated next.
Freshness source: source_snapshot records export timestamp, input hash, row count, retrieval version, prompt version, and stale reason.
API endpoint: /api/reports/workflow-audit
Owner: Operations lead for freshness; workflow owner for decision quality.
Cutover gate: Two clean weekly refreshes, owner accepts the bottleneck labels, and the endpoint proves it reads only hot mart tables.
Audience: Project lead, direct manager, and cross-functional stakeholders.
Cadence: Refresh after recurring meetings and summarize weekly.
Decision it drives: Decide which follow-ups need an owner, a written update, an escalation path, or a decision-log entry.
Freshness source: meeting_source_snapshot shows last export time, expected cadence, row count, file hash, and stale status.
API endpoint: /api/reports/stakeholder-follow-through
Owner: Project lead owns status; data steward owns mart refresh.
Cutover gate: Every open follow-up has owner and due-window coverage, and stakeholders accept the weekly summary format.
Audience: Manager, IC, sponsor, or operating group that needs a concise progress record.
Cadence: Weekly review with a monthly rollup for broader visibility.
Decision it drives: Select which decisions need reinforcement, broader communication, additional evidence, or a blocker note.
Freshness source: decision_snapshot_manifest stores decision count, newest update timestamp, review state, and stale decision flags.
API endpoint: /api/reports/decision-visibility
Owner: Manager or operating lead owns the report; IC owns evidence notes for their workstream.
Cutover gate: The report has replaced the manual weekly notes only after one month of accepted decision reviews.
Audience: AI workflow owner responsible for prompt versions, review queues, and promoted labels.
Cadence: Refresh after each labeling batch and review before labels reach a public dashboard.
Decision it drives: Decide whether to promote labels, rerun a prompt version, reduce queue scope, or hold a report in draft.
Freshness source: label_run_manifest tracks batch time, prompt version, retrieval index version, accepted count, and stale queue reason.
API endpoint: /api/reports/labeling-quality
Owner: AI workflow owner and reviewer lead.
Cutover gate: No downstream report can use new labels until reviewed rows are promoted and stale queue warnings are clear.
Run this checklist before moving a report from draft to ready or live.
Every dashboard or report declares audience, cadence, decision it drives, required marts, status, and cutover gate before any chart is built.
Each visual and drilldown maps to a bounded DuckDB, SQLite, Parquet, JSON, or API read over a hot mart.
Freshness source shows snapshot time, row counts, input hashes, retrieval versions, prompt versions, and stale reasons.
Materialized retrieval outputs store embeddings, BM25, RRF, snippets, and accepted evidence before the report opens.
The LLM labeling queue promotes only reviewed labels into report marts; draft labels stay out of live dashboards.
The API endpoint returns cached report data and never rebuilds facts, reruns retrieval, or labels records in the request path.
The owner can explain what happens when the report is stale, empty, disputed, or no longer useful.
The cutover gate proves that the old manual workflow can be retired without losing evidence, accountability, or review state.
Use the hot marts guide to shape report-ready tables and the performance guide to keep local dashboard serving fast on normal office laptops.
It is a simple register for dashboards and reports. Each entry names the audience, cadence, decision, visuals, drilldowns, required marts, freshness source, API endpoint, owner, status, and cutover gate.
A decision field prevents dashboards from becoming passive chart collections. It makes the report prove which operating choice, escalation, or follow-through action it supports.
Required marts tell the UI which bounded DuckDB, SQLite, Parquet, JSON, or API-ready tables it can read. Freshness source tells users whether the data is current enough to trust.
A cutover gate is the proof required before the report replaces an old workflow. It usually covers owner review, freshness checks, stable APIs, and no live AI work in the request path.
Start the next dashboard request by filling the catalog row. If the audience, decision, marts, freshness source, owner, and cutover gate are unclear, the report is not ready.
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