Product
Analytics & Intelligence
Know what's working. Know what's next.
Without it: the answer is already in your systems. You're just finding it a quarter too late to act on it.
What we actually build.
We turn the data scattered across your CRM, ERP, spreadsheets, and inboxes into a live intelligence layer — one that surfaces hidden patterns, predicts what happens next, and flags problems before they cost you money. Not another dashboard nobody opens. Analytics wired into how decisions actually get made.
How it gets built
- 1
Data estate audit: where your data lives, what state it's in, what it's worth
- 2
Pipeline engineering: unify sources into a clean, queryable foundation
- 3
Predictive modeling: demand forecasting, churn risk, revenue leakage detection
- 4
Decision integration: insights delivered inside the tools your team already uses
- 5
Governance guardrails: lineage, access controls, and audit readiness built in
The answer is already in there.
Not a dashboard nobody opens. The useful output of this work is a sentence — the job you run most is not the job that pays, the customers who lapse all lapse at the same point, this month is short and here is why. Your systems each hold half of it.
Margin by job type
Last 90 days
- Water heater replacement38%
- System install34%
- Duct repair27%
- Maintenance tune-up42% of your jobs19%
- Diagnostic call-out11%
Tune-ups fill more of your board than anything else and sit second from the bottom on margin. Your CRM knows the volume and your books know the margin. Neither one says this.
An illustration. The job types and the numbers are made up — the finding is the shape of what comes out when your own systems are read together.
Analytics & Intelligence
What this looks like across forecasting, risk detection, and reporting
Walmart's self-healing inventory system — which reroutes stock automatically before a shelf goes empty or excess becomes waste — has already saved the company more than $55 million.¹ The underlying idea works at any scale: data that acts, not just data that's displayed.
Predictive Forecasting
Demand, staffing, and cash flow forecasts built from spreadsheets update whenever someone remembers to update them. A live model pulls from your actual transaction data continuously, so the forecast reflects this week, not last quarter.
Anomaly & Risk Detection
The problems that cost the most money are usually visible in the data weeks before anyone notices them manually. The system flags the pattern — a margin slipping, a vendor cost creeping up, a churn signal — while there's still time to act on it.
Decision-Embedded Reporting
A dashboard nobody opens isn't an insight, it's a chore. Insights get delivered inside the tools your team already works in — a CRM alert, a Slack message, a flag on the record itself — instead of a report waiting to be checked.
Questions we get every time.
We already have dashboards — how is this different?
Dashboards show you what happened. This is built to flag what's about to happen and act on it, delivered where your team already works instead of a report they have to remember to check.
Do you need clean data to start?
No — the first phase is a data-estate audit specifically because most companies' data isn't clean. We build the pipeline that gets it usable.
Is this realistic without a data science team?
Yes — we build and hand off the system, including the monitoring and governance, so you don't need an in-house data science team to run it.
How do you handle data governance and access controls?
Lineage, access controls, and audit readiness are built into the system from the start, not added after the fact.
Sources
- 1.Complete AI Training, reporting on Walmart's Self-Healing Inventory AI deployment (2026)
Related reading
All insights
This one is already costing you. The only question is how long.
One call. We'll tell you what this would take to build in your business — or that it isn't worth building yet.