AI data analytics that turns your own numbers into decisions
Rinaztec builds AI data analytics and predictive analytics systems for businesses worldwide. We connect the data you already have in spreadsheets, databases, your CRM or accounting system, clean it, and build forecasts, alerts and dashboards your team can act on. Managers can ask questions in plain English, such as which customers are likely to leave this quarter, and get an answer with the numbers behind it.
- PRICING
- Fixed quote
- TIMELINE
- 4-8 weeks
What is AI data analytics?
AI data analytics uses machine learning and large language models to find patterns in business data, predict what is likely to happen next and explain the result in plain language. Predictive analytics is the forecasting part: demand, cash flow, churn or stock levels. The model is only as good as the data, so most of the work is joining and cleaning the sources first.
Analytics systems we build
Demand and sales forecasting
Forecast orders, stock or staffing by week and location, so you buy and schedule ahead of demand instead of reacting to it.
Churn and risk prediction
Score customers or accounts by how likely they are to leave, pay late or default, and alert the right person in time to act.
Ask your data in plain English
A chat interface over your database that writes the query, runs it safely and explains the answer with a chart.
Automated management reports
Weekly and monthly reports drafted automatically from live data, with the changes that matter called out in words.
Anomaly detection
Spot unusual transactions, sudden drops in sales or spikes in costs as they happen, not at month end.
What's included
- Data audit and a joined, cleaned data model
- Forecasting or scoring models tested on your history
- Dashboard built around the decisions you make
- Plain-English question interface with safe, read-only queries
- Alerts by email, Slack or Teams
- Documentation of every metric and model
- Full source code and data pipelines handed to you
How AI Data Analytics projects run
- 01
Data audit (weeks 1-2)
We look at what data you have, how clean it is and which decision the first model should support.
- 02
Pipeline and model (weeks 2-5)
We join the sources, build the model and test it against past results so you see how accurate it would have been.
- 03
Dashboard and alerts (weeks 4-7)
We put the results where your team works: a dashboard, alerts and plain-language reports.
- 04
Launch and retrain
We go live, track accuracy against what really happens and retrain on new data on a schedule.
How much does AI data analytics cost?
Cost is driven mostly by your data: how many sources there are, how clean they are, and how often they update. A single forecast built on one clean source is a short project. Several messy sources joined into a reporting layer take longer. We look at your data during the free call and give you a fixed quote for the first useful model.
Why businesses choose Rinaztec
In-house engineers
The people on your call are the people writing your code. No outsourcing, no hand-offs.
Preview in week one
A working preview link early and weekly demos, so you always see real progress.
Fixed, honest quotes
You approve the plan and budget before we write code. No surprise invoices.
You own everything
Full source code, accounts in your name, no lock-in and no licence fees to us.
AI Data Analytics questions, answered
How much data do I need for predictive analytics?
It depends on what you predict. A sales forecast usually needs at least one to two years of history to capture seasons. We check your data in the audit and tell you plainly if it is not enough yet.
Can I ask my database questions in plain English?
Yes. We build an interface that turns your question into a safe, read-only query, runs it, and shows the answer with the numbers and query behind it so you can check it.
Do you replace our BI tool?
Not necessarily. We can feed forecasts and AI summaries into the tool you already use, such as Power BI or Looker Studio, or build a dashboard if you have none.
How accurate are the forecasts?
We measure that before launch by testing the model on past periods it has not seen, and we show you the error range. Accuracy is tracked after launch too.
Is our data shared with AI providers?
Only what is needed, and many models run without sending raw data out. We can keep data in your own cloud and region.
Who owns the models and pipelines?
You do. Code, models, pipelines and documentation are handed over in full.
Tell us what you want to build
A free 30-minute call with an engineer. We'll tell you what it takes, what it costs, and whether we're the right fit, even if we're not.