For revenue teams

Follow the revenue. Find the next move.

Track pipeline stages, measure conversion rates, and compare quarter-over-quarter performance. Your AI analyst connects CRM and warehouse data in one workspace.

Plan your evaluation Explore the sample

See the revenue workflow

Win rate · sample

31.2%

Revenue workspaceSample data

Pipeline by stage

Qualified

128

Discovery

Proposal

Closed won

This quarter · opportunity count

crm.opportunitiesSource in context

Ask with Kole

Where are our deals stalling?

A shared view of the pipeline

One platform, a connected workflow

Built for the way your team works.

Bring pipeline, performance, and the next review into one workspace.

Quantum Lab

Query with SQL, explore with Python, and explain the result in one notebook. Built for data and development teams who want to see how an answer was reached.

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Dashboards

Turn your queries into charts, add the context behind the numbers, and build a dashboard your team can return to for the next review.

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Saved Queries

Save useful SQL, rerun it against its connection, and return to past results. Share the specific run your teammate needs to see.

Explore Saved Queries

Made for revenue

The details that make work repeatable.

From the first question to the next review, keep the source, the method, and the decisions connected.

“Where are deals stalling?”

“What drove this quarter?”

“How is each segment converting?”

See what moved the forecast.

Inspect pipeline and performance questions with account context beside the metrics.

Explore the segments behind growth.

Use SQL, Python, and charts to compare the groups that explain the revenue movement.

Keep the team on the same page.

Turn the analysis into a shared review with reusable queries and dashboards.

Inspect stage-to-stage conversion

Group opportunities by stage and compare counts with deal value. Follow the underlying SQL to understand whether growth comes from more pipeline or better conversion.

Compare periods and segments

Use date and segment filters to compare closed-won revenue, average deal size, and win rates. Keep the definitions visible when the team changes its forecast assumptions.

Keep the forecast review reusable

Save the query, chart, and commentary together. At the next pipeline meeting, update the scope and rerun the analysis instead of rebuilding a spreadsheet and slide.

Human review, built in

Your team stays in control.

Keep the target, proposed operation, and review decision in view. Kole helps investigate and propose the work; your team approves configured consequential actions.

A clear approval gate

Inspect the statement and arguments. Approve, edit, or reject the proposal before execution.

A defined workspace

Keep connections and saved work within the team context and permissions that govern access.

Explore approval workflows

CHG-024 · Sample ticket

Add a renewal date

ALTER TABLE contracts
ADD COLUMN renewal_date date;

ConnectionProduction · PostgreSQL

ReviewerJamie Chen

Awaiting human approval

Connected to your work

Start with the data you already have.

Explore connected databases and files, inspect the schema, and carry the source context into your analysis. Your question can span more than one system.

Browse records and relationships with Data Explorer before choosing the query.
Use Tangents for supported federated queries across databases and files.

Explore connected data

The workflow in practice

Explore pipeline and performance questions together.

Teams

RevOps & Sales

Sources

CRM + Warehouse

Analysis

Pipeline + QoQ

Output

Charts + SQL

Revenue teams live between systems. The CRM tracks deals. The warehouse holds product usage, billing events, and historical closes. Spreadsheets patch the gap — pivot tables built on stale exports, shared files with formulas that break when someone adds a column. Every Monday, the pipeline review starts with "let me pull the latest numbers," and by the time the deck is ready the data is already a day old.

SyneHQ connects your CRM and warehouse data in one workspace so the question and the answer stay in the same place. Write SQL against your connected sources, let Kole surface schema and table context, and turn the result into a chart or table without exporting anything to a spreadsheet first.

Pipeline visibility from source to close

The first thing a revenue team needs is a clear view of what is in the pipeline right now — not last week's snapshot pasted into a slide, but a query that runs against the current state of the data.

In Quantum Lab, start with a SQL block that groups pipeline deals by stage. The query is visible, the connection is named, and the result is a DataFrame you can inspect before presenting it. Add a Chart block to see the shape of the funnel: where deals are stacking up, where they are falling out, and how the distribution compares to the prior quarter.

Kole can inspect your connected schema and propose queries when you describe the question. Ask it to break down pipeline by stage and amount, and review the SQL it generates before running it. The notebook keeps the query, the result, and any written context together so a teammate reviewing the analysis can trace how the numbers were produced.

Pipeline analysis

Q3 pipeline by stage

Illustrative data

Stage conversion funnel

Lead312 deals · $4.2M

Qualified187 deals · $3.1M

Proposal94 deals · $2.4M

Negotiation41 deals · $1.8M

Closed Won27 deals · $1.3M

Conversion: Lead → Qualified 60% · Qualified → Proposal 50% · Proposal → Negotiation 44% · Negotiation → Closed 66%

SQL query

Pipeline stage summary

SELECT stage, COUNT(*) AS deals, SUM(amount) AS total
FROM pipeline
WHERE quarter = 'Q3'
GROUP BY stage
ORDER BY stage_order;

CONNECTION: CRM Warehouse
ROWS: 5 stages
QUARTER: Q3 2026

A pipeline view built this way is not a static chart — it is a query with a visible source. When the definition of "qualified" changes or the team adds a new stage, update the SQL and rerun. The chart follows the data, not the other way around.

What to look for in a pipeline query

Stage distribution: Are deals concentrated in early stages or progressing through? A healthy pipeline has movement across stages, not a pile-up at the top.
Stage-to-stage conversion: What percentage of deals move from Qualified to Proposal? From Proposal to Negotiation? Conversion rates by stage surface where deals stall.
Amount by stage: Total count matters, but weighted amount by stage shows where the revenue sits. A small number of deals in Negotiation can represent more value than a large number of Leads.

Quarter-over-quarter without the spreadsheet

The second question revenue teams ask constantly is "how does this quarter compare to last quarter?" That comparison usually lives in a spreadsheet with two tabs, manual formulas, and a chart that someone rebuilds every quarter.

In SyneHQ, write the comparison as a query. Pull closed-won deals grouped by month, filter to the current and prior quarter, and let the result show the trend. A Dashboards chart can display monthly revenue side by side. A Quantum Lab notebook can hold the query, a Python block for calculating deltas, and a Markdown block that explains what changed.

Performance comparison

Q2 vs Q3 2026

Illustrative data

Quarter-over-quarter

Metric	Q2	Q3	Delta
Closed-won revenue	$1.82M	$2.14M	+17.6%
Deals closed	23	27	+17.4%
Avg deal size	$79.1K	$79.3K	+0.2%
Win rate	14.2%	14.4%	+0.2pp
Avg cycle (days)	38	41	+7.9%

Monthly closed-won revenue (Q2 + Q3)

Q2 Q3

SQL query

Monthly closed-won revenue

Read only

SELECT date_trunc('month', closed_at) AS month,
       SUM(amount) AS revenue
FROM deals
WHERE status = 'won'
GROUP BY 1
ORDER BY 1;

This approach means the quarter-over-quarter comparison is reproducible. When Q4 starts, change the date filter and rerun. The methodology, the source, and the presentation are all in one place — not scattered across a spreadsheet, a slide, and an email thread.

Metrics that matter for QoQ review

Closed-won revenue by month: The most direct measure of performance. Plotting monthly totals across two quarters shows trajectory and seasonality.
Average deal size: Is growth coming from more deals or bigger deals? This distinction shapes whether the team needs more pipeline or better deal execution.
Sales cycle length: Are deals closing faster or slower? A lengthening cycle in a growing quarter can signal future problems even when the top-line number looks good.
Win rate: The ratio of closed-won to total opportunities created. Compare this quarter's rate to last quarter's to see if the team is converting more efficiently or if growth is masking a declining rate.

Questions revenue teams ask every week

SyneHQ is built for the questions that come up in every pipeline review, forecast call, and board prep. These are not abstract analytics problems — they are specific questions with specific data behind them.

Pipeline by region or segment: "What does the pipeline look like in EMEA versus North America?" Group by region, filter by quarter, compare stage distributions. A single query replaces a set of filtered CRM views.

Expansion versus new business: "How much of this quarter's pipeline is expansion revenue versus new logos?" Tag deals by type in your CRM or warehouse, then group and compare. Understanding the mix changes how you forecast.

Win rate by segment: "Are we winning more in mid-market or enterprise this quarter?" Calculate win rate by segment and compare across quarters. A declining win rate in a growing segment is an early warning.

Rep performance and quota attainment: "Which reps are on track for quota and which are behind?" Join deal data with rep assignments and quota targets. A Dashboards chart showing attainment by rep gives the sales leader a clear view without asking each manager for an update.

Forecast accuracy: "How close was last quarter's forecast to the actual number?" Compare the forecast snapshot to the final closed-won total. Tracking this over time shows whether the forecasting process is improving or consistently optimistic.

Each of these questions starts with a SQL query against connected data, produces a result you can inspect, and can be saved as a notebook or dashboard for the next review cycle.

From question to answer in one workspace

The pattern for revenue analysis in SyneHQ is consistent:

Connect your CRM and warehouse data sources.
Query with SQL in Quantum Lab or Dashboards — or ask Kole to propose the query.
Inspect the result as a table, then configure a chart when the visual comparison helps.
Document the context — what the query measures, which filters are applied, what the team should look for.
Reuse the notebook or dashboard in the next review cycle by updating the date filter and rerunning.

Revenue teams should not need to be SQL experts to benefit. Kole can inspect available schemas and propose queries from a natural-language question. The notebook keeps the SQL visible so anyone can verify what ran, but the starting point can be a question, not a query.

Start exploring pipeline and performance questions in Quantum Lab, or build a recurring revenue dashboard in Dashboards.

Forecasting notebooks in development

We are preparing forecasting starters that take a saved revenue metric into an inspectable Python notebook. The proposed workflow checks historical coverage, compares a model with simple baselines, evaluates an untouched holdout, and reports approximate prediction bands with their limitations. Short or incomplete history should produce a clear limitation, not a confident invented forecast.

These templates are pending release validation. Read about the approach and its current limits on the Kole product page.

Resources

Take the next question further.

Practical guides, reusable starting points, and product details.

Practical guide

Ask business questions with AI and SQL

Explore resource

Workflow library

Start from an analysis template

Explore resource

Product documentation

Explore the SyneHQ documentation

Explore resource

Customer perspective

More time for the work that matters.

With their robust platform, we've seamlessly integrated multiple data sources, unified analytics, and automated standard reporting, saving us countless hours each week.

Mukul · Co-Founder & CEO, Thirdbase