Know your numbers on Monday morning, not three months later.

Every small business already collects enough data to make far better decisions than it makes. The data is just scattered, inconsistent, and locked inside tools that each report on their own narrow slice. The work here is less about clever analysis and more about getting to one honest set of numbers, which is why we start with a connected data layer. Scheduled syncs pull from the systems of record — CRM, booking or job software, point of sale, accounting, ad platforms, call logs — into one place on a fixed cadence. Fields get reconciled and definitions get pinned down early, because most disagreements between systems are definitional, not technical. What counts as a lead. When a job is "closed." Whether revenue is booked at quote, at completion, or at payment. We write those definitions down and every report uses them, which is what makes the numbers arguable in a useful way instead of simply distrusted.

The dashboard is deliberately small. Six to eight numbers, chosen because a decision hangs on each one: leads by source, response time, quote-to-close rate, booked revenue against capacity, average job value, cash position and receivable ageing, and one or two that matter only in your trade. Each has a comparison — last week, same month last year — because a bare figure means nothing without a baseline. It's built mobile-first, since you'll look at it in a truck or between appointments far more often than at a desk.

Reporting is pushed, not pulled. A dashboard nobody opens changes no decisions, so a short written summary lands in your inbox on a schedule: what moved, by how much, and what's worth attention. AI drafts the narrative from the actual figures — it summarizes and flags, it does not invent context — and the numbers in it are always linked back to the source records so anything surprising can be traced to the jobs behind it.

Forecasting and anomaly detection sit on your own history, not on an industry average. Seasonality, recent trend, and pipeline feed simple models that project demand, cash, and staffing needs a few weeks and a few months out, always as a range rather than a false-precision single number. Anomaly detection is separate and blunt on purpose: statistical thresholds on the metrics that matter, alerting when something falls outside its normal band. A 30% dip in inbound calls is worth knowing about on the Wednesday it starts, not in next quarter's review.

We're candid about the limits. AI-assisted analysis is a good early-warning system and a poor oracle. It will tell you that bookings from one channel dropped; it will not know that the referral partner behind them retired. The value is that you get the question three weeks earlier, while it's still cheap to answer.