Product
Advanced analytics.
We capture every system your business runs on into infrastructure you own, model it, and build the reporting on top. Agents do the work nobody has time to do. Analytics is how your team sees what happened, on exactly the same data.
Everything below runs live in your browser on sample data. No login, no setup.
Capture, first
Every system your business runs on, pulled into one place you own: CRM, ERP, accounting, support, product, bank, warehouse, and the spreadsheets nobody will give up. 100+ prebuilt sources, and custom connectors typically inside two weeks.
Modeled and governed
The data gets modeled around your entity structure and the questions your team actually asks, with role-based access, audit trails, and lineage on every value. This is the part BI tools assume you already did.
Dashboards on live data
Reporting that updates the moment your systems do. Drill any number back to the source record in the CRM, ERP, or warehouse that produced it, so nobody reconciles three versions of the truth before a meeting.
Pivots on a real SQL engine
Drag fields into rows, columns, and values the way you already do in a spreadsheet. Underneath it runs against your governed data on real infrastructure, so it stays fast on millions of rows rather than the few thousand a workbook can hold.
Workflows that run themselves
Define the pull, the filter, and the shape once. The workflow runs on a schedule and lands the result where your team already works, without anyone re-doing the export by hand every month.
The same layer your agents read
Every dashboard, pivot, and workflow sits on the governed model your agents query. People and agents never end up looking at different numbers, which is the failure mode that quietly kills trust in both.
Create a workflow once. Load the right data into Excel every day.
This is a live sample P&L running entirely in your browser. Drag the nodes, follow the flow, and watch how pivoting by segment exposes the business line quietly compressing margin. No login, no setup.
| Segment | Jan | Feb | Mar | Apr | Total |
|---|---|---|---|---|---|
| Sell-side | $100k | $109k | $123k | $137k | $469k |
| Buy-side | $60k | $62k | $63k | $67k | $252k |
| Property Mgmt | $7k | $8k | $9k | $10k | $34k |
| Leasing | $2k | ($4k) | ($11k) | ($19k) | ($32k) |
| Total | $169k | $175k | $184k | $195k | $723k |
This is a taste on sample data. Build your own workflow →
The familiarity of Excel, with the processing power of enterprise SQL.
Build a pivot the way you already do in a spreadsheet: drag fields into rows, columns, and values. Underneath, Go Fig runs it on a real SQL engine against your governed data, so it stays fast on millions of rows, not just the few thousand a workbook can hold.
Drag fields here to filter
SELECT
segment,
SUM(CASE WHEN close_month = 'Jan' THEN commission END) AS "Jan",
SUM(CASE WHEN close_month = 'Feb' THEN commission END) AS "Feb",
SUM(CASE WHEN close_month = 'Mar' THEN commission END) AS "Mar",
SUM(CASE WHEN close_month = 'Apr' THEN commission END) AS "Apr",
SUM(commission) AS "Total"
FROM deals
GROUP BY segment
ORDER BY "Total" DESC;| Segment | Jan | Feb | Mar | Apr | Total |
|---|---|---|---|---|---|
| Property Mgmt | $116k | $121k | $126k | $131k | $494k |
| Leasing | $92k | $97k | $102k | $107k | $398k |
| Buy-side | $68k | $73k | $78k | $83k | $302k |
| Sell-side | $44k | $49k | $54k | $59k | $206k |
| Total | $320k | $340k | $360k | $380k | $1400k |
This is the real pivot builder, running on sample fields. Try it on your own data →
Questions about the analytics layer
What it is, how it differs from a BI tool, and how it connects to the agents.
Is this a product I can log into?
The analytics layer is real software, and your team works in it every day. But it is not something you buy off a shelf and wire up yourself. We build it during the engagement: the connections, the data model, the metric definitions, and the dashboards your team actually needs. Then we stay and run it with you.
How is this different from a BI tool?
A BI tool hands you an empty canvas and assumes your data is already captured, cleaned, modeled, and joined. That assumption is where most BI projects die. We do the capture and the modeling first, so the dashboards have something trustworthy underneath them. Visualization is the last five percent of the problem, not the first.
Why isn't data capture its own thing?
Because capture on its own is a plumbing project with no payoff, and analysis on its own is the empty canvas nobody fills in. They are one argument: we capture your data so there is something real to analyze, and we build the analysis so the capture earns its keep. The agents then read the same layer.
Do we have to give up Excel?
No, and we would not ask. Excel is the most flexible analysis tool ever built. Bi-directional sync keeps the models your team already made running on live data, and direct access means pulling any metric, any cut, any period into a workbook without exporting a CSV or waiting on someone to write SQL.
How does this relate to the agents?
Same foundation. Agents read the governed data model that the dashboards and pivots run on. That is the point: when an agent flags something and a human opens the dashboard to check it, they are looking at the same numbers. Analytics is how people see the data. Agents are how the work gets done without someone watching.
Book a free discovery session on your sales system
A senior advisor walks your sales system with you: where the data actually lives, where the work piles up, and what is falling through. You leave with a map of the highest-value optimizations and a real read on what it would take to build them. No pitch deck, no slideware.