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AI work dashboards stay fast when the UI reads small serving tables. Precompute overview, root cause, resolution, account-risk, prevention, and top-N similar-item tables so page loads never query the full universe live.
The most useful AI workplace dashboards answer repeated operating questions: what changed, why it changed, what needs resolution, which accounts need attention, what should be prevented, and which similar examples explain the pattern. Those questions should not trigger a scan of every source row.
A hot marts serving layer precomputes those answers into narrow tables. DuckDB can rebuild the analytical facts, SQLite can preserve review state, retrieval can be materialized, labels can be accepted, and the UI can stay boring: read the latest serving table and show freshness.
If a dashboard request has to rebuild facts, run retrieval, label records, or summarize the full universe, the serving layer is missing. The hot path should read bounded tables and make stale data obvious.
These serving tables are intentionally narrow. Each one maps to a dashboard question and can be refreshed, reviewed, and tested before anyone opens the UI.
One row per team, workflow, account, or workstream per snapshot day.
Precompute the overview before the dashboard opens; the UI reads this small serving table instead of scanning the full universe.
One row per issue cluster, root cause, owner group, and snapshot.
Root cause summaries refresh in the analysis batch, not in the detail drawer, so the same evidence stays stable during discussion.
One row per unresolved item with owner, due window, blocker type, and accepted next action.
The resolution queue is sorted during refresh so the dashboard can render the first page without recomputing priority.
One row per synthetic account-risk signal, account, stakeholder group, and snapshot.
Account-risk rows are capped to the visible review horizon so the UI never queries every account or every historical touchpoint live.
One row per prevention opportunity, workflow, policy gap, or repeated handoff failure.
Prevention rows are generated from accepted summaries and labels so the dashboard presents durable system changes, not fresh speculation.
Up to N rows per selected item, retrieval query, similarity method, and snapshot.
Top-N similar-item rows are selected before page load so detail views read a small serving table instead of rebuilding retrieval live.
The refresh plan is the boundary between broad analysis and fast daily inspection. It turns a large universe of workplace artifacts into dashboard-ready hot marts.
Take a dated snapshot of the broad source universe before analysis starts so counts, labels, and examples do not shift while the dashboard is open.
Snapshot manifest with source ids, row counts, hashes, and freshness timestamps.
Use DuckDB for the heavier joins, window functions, deduping, trend buckets, and retrieval preparation across CSV, JSON, and Parquet inputs.
Narrow fact tables for work items, meetings, decisions, accounts, follow-ups, and source documents.
Use SQLite for row-level app state such as accepted labels, hidden examples, queue status, pinned rows, and refresh history.
Durable review tables that survive restarts and keep the UI from treating provisional model output as final.
Run embeddings, BM25, RRF, materialized retrieval, and the LLM labeling queue outside the request path, then promote only reviewed outputs.
Accepted retrieval bundles, reviewed label tables, and rejected-output logs.
Precompute the exact overview, root cause, resolution, account-risk, prevention, and top-N similar-item tables the dashboard will read.
Small serving tables in DuckDB, SQLite, Parquet, or JSON with bounded row counts and stable sort keys.
Write down what each screen can read and what it must never compute. This keeps product changes from smuggling expensive AI work back into the hot path.
Use this before adding a new dashboard panel or AI-generated summary to the serving layer.
The serving layer maps every visible dashboard panel to one small serving table or one bounded read over a serving table.
Overview, root cause, resolution, account-risk, prevention, and top-N similar-item tables are precomputed before the UI opens.
The dashboard can show freshness, row count, prompt version, retrieval version, and reviewer status without running the pipeline.
DuckDB handles analytical rebuilds while SQLite handles lightweight review state and app-like updates.
Embeddings, BM25, RRF, materialized retrieval, and the LLM labeling queue run outside the dashboard request path.
When a serving table is stale or empty, the UI shows the refresh boundary instead of querying the full universe live.
Use analysis mode to rebuild the marts and presentation mode to inspect the accepted snapshot. Use the performance guide when the laptop budget is the constraint.
A hot mart is a small precomputed table shaped for a dashboard screen. It holds the facts, labels, ranks, summaries, and freshness fields the UI needs without scanning the full source universe live.
Start with overview, root cause, resolution, account-risk, prevention, and top-N similar-item tables. Those six tables cover the most common operating questions without pushing heavy retrieval or labeling into the UI.
Use DuckDB for analytical rebuilds across files and use SQLite for review state, queue status, pins, hidden rows, and small app-like updates. The serving layer can read from either as long as the tables are bounded.
No. Those steps belong in the refresh batch. The dashboard should read accepted materialized retrieval outputs and reviewed labels, then show freshness if the serving tables are stale.
Start with one overview table, one root cause table, one queue, and one similar-item table. Add more marts only when a real dashboard screen needs a bounded read.
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