Explore

What ships

• Chart — a saved SQL query against one database Connection plus a visualization choice (table, scalar, bar, line, area, pie).
• Dashboard — a grid of Charts, each one a DashboardCard with its own size and position.
• Run — every execution (ad-hoc from the editor or from a saved Chart) goes through the same executor with a 30s wall-clock timeout and a 5,000-row cap.
• Data browser — the Chart editor reads the tables, views, columns, and data types visible to the selected Connection. Search it, expand a table, insert a quoted column name, or preview a table without writing the starter query yourself.
• AI-native workflow — grant an AI Employee a database Connection, then ask them to inspect its schema, validate SQL, save Charts, and assemble a Dashboard that appears in the same Explore UI Members use.

What you need first

Explore reads from Integrations — specifically Connections of provider postgres, mysql, or clickhouse. Set one up under Explore → Integrations, then it shows up in the Connection picker inside Explore. To delegate analytics, click Build with AI, choose the Connection and an AI Employee, and Genosyn can create the Connection Grant before opening Chat.

Building with an AI Employee

1. Open Explore and click Build with AI.
2. Choose a connected database and an AI Employee. If they do not already hold its Connection Grant, the action is labelled Grant & open chat and creates it before continuing.
3. Review or edit the starter request. Chat opens with the request as a draft; no model runs and no data is queried until you send it.
4. The employee lists its granted Explore Connections, inspects the selected schema, validates each query, saves reusable Charts, creates a Dashboard, and pins the Charts in a readable order. The results appear immediately in Explore and every write carries the AI employee in its audit trail.

Authoring a Chart

1. Open Explore from the sidebar, click New chart.
2. Pick the Connection that holds the data.
3. Use the Data browser to search the Connection's visible tables and columns. Expand a table to inspect its data types. Click a column to insert its safely quoted name at the SQL cursor, or click the eye beside a table to build and immediately run a SELECT * … LIMIT 100 preview.
4. Write or refine the SQL, then click Run. Cmd/Ctrl + Enter runs from the editor; Cmd/Ctrl + S saves. Query errors and the elapsed time stay inline so you can iterate without leaving the page.
5. After a successful Run, Explore may suggest a Number, Line, or Bar visualization from the result shape. Accept it in one click or choose any visualization yourself; nothing changes automatically behind your back.
6. Configure the visualization (dimension column, measure column(s), stacking, prefix or suffix), then Save. Explore warns before a link takes you away from unsaved edits.

The six visualization types

table

Raw rows. Good fallback when the data doesn't have an obvious shape — and useful as a sanity check before picking a richer viz.

scalar

A single big number. Reads the first cell of the first row. Use for KPIs: MRR, weekly signups, p99 latency.

bar

Categories on one axis, measure on the other. Configurable orientation (vertical / horizontal) and stacking when there are multiple measure columns.

line

Time on the x-axis, measure on the y-axis. Best for any series indexed by a date or timestamp.

area

Like line, but filled. Better for cumulative or volume-style series where you want the whole shape to feel weighty.

pie

Share of total across a single dimension. Don't reach for it when bar would do — pie is rarely the right call for more than four or five slices.

Building a Dashboard

1. From Explore, click New dashboard. Add a title and an optional description, then create it.
2. Click Add chart and choose a saved Chart. Charts already on the Dashboard are marked and cannot be added twice. If none exists yet, jump straight to the Chart editor from the picker.
3. In Edit mode, use the arrow controls to move a card and the named size menus to resize it. Edit the title in place when the Dashboard needs a shorter label than the saved Chart. Cards collapse into a readable single column on smaller screens.
4. Hit Done. Refresh reloads every card, while the refresh button on an individual card reloads just that Chart — both use the same 30s / 5,000-row envelope as the editor.

Sharing with AI Employees

Explore has two grant boundaries. A Connection Grant lets an AI Employee inspect that database, run ad-hoc SQL, create a Chart against it, and change that Chart's SQL. A Chart or Dashboard Grant controls access to the saved Explore row. Charts and Dashboards default to read for every employee in the company; their AI author receives write.

Open the Share menu on any Chart or Dashboard to change a teammate's level, revoke a grant, or invite an employee who didn't default to access. Manage a database Connection Grant from Build with AI, the Connection's Manage access view, or the employee's Connections tab.

MCP tools

Every employee gets these via the built-in genosyn MCP server (subject to the grants above):

• list_explore_connections, get_explore_schema, and run_explore_query — the discovery and validation loop over database Connections explicitly granted to that employee. Credentials are never returned.
• list_charts, get_chart, run_chart — read paths. The run_chart tool is the one most teams hit: a teammate asks "what was MRR last month?", the employee finds the right Chart and runs it.
• create_chart, update_chart, delete_chart — write paths. Create requires a Grant on the bound Connection; changing SQL requires both that Connection Grant and write on the Chart. Other edits and deletion require write on the Chart.
• list_dashboards, get_dashboard, create_dashboard, add_dashboard_card — dashboard authoring.

Limits

Query timeout

30 seconds, wall-clock. Long-running analytical scans should hit a precomputed table, not the live OLTP db.

Row cap

5,000 rows per query. Larger result sets are truncated server-side. Aggregate before you return, or paginate via SQL OFFSET.

Connectors

Postgres, MySQL, ClickHouse. Snowflake / BigQuery / Redshift are on the roadmap.

No parameters yet

Charts run their SQL verbatim — no :start_date / :customer_id placeholders. Use a SQL view that joins against a date table if you need parameterization today.

Quick recipes

Recurring revenue scalar

SELECT
  ROUND(SUM(amount_cents) / 100.0, 0) AS mrr_usd
FROM subscriptions
WHERE status = 'active'
  AND interval = 'monthly';

Viz: scalar. Pin to a dashboard alongside other finance KPIs.

Weekly signups, bar

SELECT
  date_trunc('week', created_at) AS week,
  COUNT(*) AS signups
FROM users
WHERE created_at >= NOW() - INTERVAL '12 weeks'
GROUP BY 1
ORDER BY 1;

Viz: bar with dimension week, measure signups. Switch to line to see the trend without the buckets.

Plan mix, pie

SELECT plan, COUNT(*) AS customers
FROM subscriptions
WHERE status = 'active'
GROUP BY plan;

Viz: pie, dimension plan, measure customers.

What's deferred

These are on the roadmap but not in v1 — call them out in an issue if you need one:

• Parameters / filters (date range, dropdown bound to a column).
• Scheduled deliveries — email a dashboard PNG at 9am.
• Embedded views — public read-only links, signed.
• Snowflake / BigQuery / Redshift connectors.
• Native (no-SQL) query builder over a column picker — for teammates who don't write SQL.
• AI-suggested charts on a freshly-added connection.