Service : automate
Numbers your team can act on.
Data and AI analytics turns scattered operational data into decisions people actually make: pipelines that keep numbers trustworthy, dashboards people open, and AI reporting that writes the narrative, not just the chart. Novistu builds analytics on your warehouse and tools, from reporting automation to predictive models.
The mechanism
Pipelines that keep data trustworthy, dashboards people actually open, and reports that write themselves on schedule.
- Sources
- Pipeline
- Metrics
- Reports
- Decisions
The problem
What this fixes
Reporting eats the analyst
Every Monday, someone exports four systems into spreadsheets to answer the same five questions. By Wednesday the numbers are already stale.
Nobody trusts the numbers
When three dashboards disagree, decisions go back to gut feel. Trust in data comes from pipelines and definitions, not from prettier charts.
Data exists, insight does not
You have years of transactions and no answers. The gap is not storage; it is modelling, definitions and the last mile into decisions.
How we build it
Principles before code.
01
Define the metric layer once
Revenue, churn, margin, SLA: agreed definitions in code, tested like software. Every dashboard reads from the same layer, so numbers agree.
02
Automate the reporting ritual
Scheduled pipelines produce the numbers and AI writes the readable summary: what changed, what matters, what to look at. Delivered to inbox, Slack or dashboard.
03
Predict where it pays
Churn, demand, capacity, risk: predictive models go in only where a decision changes because of them, and they ship with monitored accuracy.
04
Own the stack pragmatically
Your warehouse, your BI tool, open transforms. No proprietary lock-in on the layer that should outlive every vendor.
Delivery
From first call to running system.
- 01
Frame
Pick the decisions the data must serve; define the metrics that drive them.
1 week - 02
Pipeline
Sources, warehouse, transforms and tests, live and scheduled.
2 to 4 weeks - 03
Deliver
Dashboards and AI narrative reports wired to the metric layer.
1 to 2 weeks - 04
Evolve
Add sources, models and alerts as the questions get sharper.
Ongoing
What you get
- Data pipelines from your sources into your warehouse
- Tested metric layer with agreed definitions
- Dashboards and scheduled AI-written reports
- Predictive models where decisions justify them
- Data quality monitoring with alerts
- Documentation of sources, definitions and lineage
Proof
Systems like this, in production.
Questions
Asked before every build.
It is the normal starting point. Part one of every engagement is making the pipeline defensive: validate at the door, quarantine the junk, and report on data quality itself so trust is earned with evidence.
Both, for different people. Dashboards serve the questions users know to ask; scheduled narrative reports serve leadership, reading what changed and why it matters without opening anything.
Yes, including Power BI, Looker and Tableau. Usually the wins are upstream (pipelines and definitions), and your existing tool gets trustworthy data for free.
When a decision changes based on the prediction: who to call, what to stock, when to staff. If no decision changes, a chart is cheaper than a model, and we will say so.
Tell us what is eating your team's hours.
A short brief, answered within one working day. The first call is free, and if AI is not the right answer, we will say so on that call.
First call free : honest about fit : no obligation