For finance teams

Review financial questions with governed context

Ask revenue, margin, and cost questions across your data warehouse. Kole investigates with read-only discovery, proposes SQL, and waits for your approval before touching financial data.

Teams: Finance & FP&A
Sources: Warehouse + ERP
Agent: Kole
Controls: Approval gates

Your finance team has questions every week that live in your data warehouse: where did revenue land relative to plan, which cost center is driving the variance, how does margin look by segment this quarter. Getting answers usually means filing a request with the data team, waiting for a slot, then going back and forth on definitions. By the time the query runs, the meeting already happened.

SyneHQ gives your finance team a way to ask those questions directly and get investigated answers without bypassing the controls that financial data requires. Kole, the AI analyst agent, starts from your question, discovers what data is available, and proposes the SQL to answer it. You review and approve before anything runs against your financial tables.

From question to revenue breakdown

Start with the question your team actually needs answered: "How does net revenue break down by segment this quarter?" Kole connects to your data warehouse, inspects the available schemas and tables using read-only discovery, and identifies the columns and joins needed to answer the question.

Instead of writing SQL yourself or explaining the request to an analyst in a ticket, you see Kole's proposed query alongside the tables it references. The query is visible and editable before it runs. If the logic needs a different revenue definition or a narrower date range, adjust it in the review step.

The result lands in a Quantum Lab notebook or a Dashboard block where your team can inspect the SQL, the output table, and any chart built from it. The investigation stays with the result so a colleague reviewing next quarter's numbers can trace exactly how the answer was produced.

Finance / revenue analysis

From a question to a revenue breakdown your team can inspect.

Proposed query

SELECT  segment,  SUM(net_revenue) AS revenueFROM  analytics.ordersWHERE  quarter = 'Q3-2026'GROUP BY  segmentORDER BY  revenue DESC;

Illustrative query - not a live connection

Result / net revenue by segment

Enterprise$4.2M

Growth$2.8M

Self-serve$1.1M

Sample data for illustration

Every financial query is governed

Financial data carries compliance, audit, and accuracy requirements that make ungoverned access a non-starter. SyneHQ keeps Kole's work inside a governed workflow at every step.

Read-only discovery runs automatically. Kole can browse connections, schemas, table structures, and saved queries without approval. This lets it understand what data is available and how it is organized before proposing any operation. Discovery never modifies data.

SQL execution requires your approval. When Kole proposes a query that runs against your financial tables, the proposal appears in an approval card. You see the SQL statement, the target connection, and the tables involved. Approve it as written, edit the arguments, or reject it with feedback so Kole can refine the approach.

Every action is recorded. Approved queries, rejections, edits, and the resulting outputs form an audit trail inside the notebook. When your auditors or controllers ask how a number was produced, the trail is already there: the question, the proposed SQL, the review decision, and the output.

This is not a blanket gate on every click. Approval applies to configured consequential actions that Kole proposes, specifically the operations that touch your financial data. Regular notebook work, writing markdown notes, arranging blocks, inspecting results, stays fluid.

Finance / governance

Every proposed query is reviewed before it touches your financial data.

Kole proposal

Run cost variance query

Awaiting review

Connection: warehouse / finance
Tables: finance.cost_actuals, finance.budget_plan
Proposed SQL: SELECT cost_center, SUM(actual) - SUM(budget) AS variance FROM finance.cost_actuals JOIN finance.budget_plan USING (cost_center, period) WHERE period = '2026-Q3' GROUP BY cost_center ORDER BY variance DESC;

Illustrative approval card - controls disabled

Audit trail

Auto

Discovery

Inspected finance.cost_actuals schema

Auto

Discovery

Inspected finance.budget_plan schema

Review

SQL proposed

Cost variance query for Q3-2026

Sample trail for illustration

Built for the questions finance teams actually ask

The value of governed data access shows up in the recurring questions your team faces every period.

Margin analysis by product or segment

"What is gross margin by product line for the last two quarters?" Kole identifies the revenue and cost-of-goods tables, proposes a query that joins them on the product key, and groups by the segment dimension your team uses. The chart shows margin side by side so the comparison is immediate.

Cost allocation and variance

"Which cost centers are over budget this month, and by how much?" Kole connects to your ERP or warehouse cost tables, compares actuals against the plan, and surfaces the variance. You see which cost categories are driving the overrun before the monthly close meeting.

Revenue recognition and period comparison

"How does recognized revenue this quarter compare to the same quarter last year?" Kole proposes a period-over-period query that respects your recognition timing. The notebook keeps both periods in the same view so the trend and any anomalies are visible together.

Ad hoc questions during planning

During budget season, questions come fast: headcount cost by department, projected run rate given current bookings, allocation splits for shared services. Each question follows the same flow: Kole discovers the relevant data, proposes the query, you review, the answer appears in an inspectable notebook. No queue, no ticket, no waiting for a slot.

Connect the tools your team needs

SyneHQ's finance workflow connects several products that work together:

Quantum Lab keeps the full investigation in an ordered notebook: SQL, results, charts, and written context in one place.
Dashboards turns approved queries into configurable charts your team can revisit and refresh.
Data Explorer lets your team browse connections, schemas, and tables when they want to understand available data before asking Kole.
Kole ties it together as the agent that investigates your question, proposes the work, and waits for your review.

Start with a question your team needs answered this week. Kole will discover the data, propose the query, and wait for you to say go.