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AI Analytics and Business Dashboards for Service Companies
CyberGipsy connects your scattered systems into one set of numbers you can trust, then puts a natural-language layer on top so you can ask questions in plain English and get an answer with the working shown. One weekly report is generated automatically and read in ten minutes.
Definition. AI analytics is the consolidation of business data into a governed model, combined with a natural-language interface that lets non-technical people ask questions and receive traceable answers.
The problem this removes
- Three systems give three different revenue numbers and nobody knows which is right.
- Reports take a day to build, so they are built once a quarter and never used.
- You measure activity, not profit.
What we actually build
Data consolidation
CRM, jobs, accounting, ads, calls and site systems into one place.
Definitions
One agreed meaning for revenue, margin, lead, qualified lead, job and win rate.
Core dashboards
Sales, operations, finance and marketing, on one screen each.
Natural-language querying
Ask a question, get an answer plus the query and the source, so it can be checked.
Weekly operating report
Generated, narrated, and sent before the meeting instead of during it.
Anomaly alerts
Unusual movements flagged when they happen, not at month end.
Benchmarks
Your performance over time, and against your own best periods.
How the install works
Five steps. Nothing here is a workshop.
Agree the definitions
The unglamorous week that makes everything after it possible.
Connect the sources
Read-only connections, no migrations.
Build four dashboards
Not forty. Fewer screens, more decisions.
Add the language layer
So people who do not build reports can still get answers.
Automate the weekly review
The report writes itself; the meeting decides things.
What changes, in numbers
Honest ranges from installs of this type. Your baseline is measured during the diagnostic so the comparison is yours, not an industry average.
| Measure | Typical before | After install |
|---|---|---|
| Definitions of revenue in use | 2-4 | 1 |
| Time to answer an ad hoc question | Hours or days | Seconds |
| Time to produce the weekly report | 2-4 h | Automatic |
| Metrics tied to profit rather than activity | Few | Most |
| Anomalies detected | At month end | Same day |
What AI will not do here
A dashboard does not make decisions and a natural-language answer is only as reliable as the model and definitions beneath it. We always show the query and the source, because an unverifiable number is worse than no number. Garbage in remains garbage out: if job costs are not recorded, no amount of visualisation will produce a margin you can trust, and we will start with the data capture instead of the charts.
AI Analytics, BI & Dashboards: questions we get asked
Do I need a data warehouse?
For a small company, usually not at first. A lightweight consolidation is enough. We add proper infrastructure when volume or complexity justifies it, not before.
Can I trust an AI answer about my numbers?
Only if you can check it. Every answer comes with the query and the underlying rows, and definitions are governed centrally so the same question always returns the same answer.
How many dashboards will I get?
Four to six. Most BI projects fail by producing dozens of screens nobody opens. We optimise for the weekly decisions you actually make.
Does it work with my existing tools?
It reads from them. Nothing is migrated and nothing is replaced. If a system has no API, we usually still get the data out via scheduled exports.
Who maintains it?
You can, once definitions are set. We maintain it as part of the retainer if you prefer, and the whole configuration is yours either way.
What is the fastest useful dashboard?
Margin by job and by service. It is the number most service businesses have never seen, and it usually changes what they sell within a quarter.
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