Demand forecasting
Item- and branch-level forecasts with reorder suggestions.
AI services
AI analytics & business insights
Demand forecasting, margin analysis and anomaly detection on your live sales and stock data — pushed to the people who make decisions, in plain language.
What we offer
What's included
Margin & profitability
See what really makes money by product, customer and branch.
Anomaly detection
Alerts for suspicious discounts, voids and cash differences.
Natural-language questions
Ask business questions in plain English or Urdu.
Know what to stock, price and fix — before it costs you
Your POS and ERP already hold the answers; AI analytics finds them. We build models on your real sales history to forecast demand per item and branch, flag unusual patterns, and explain what is driving your numbers.
What you get
- Demand forecasting and automatic reorder suggestions to cut stockouts and dead stock
- Margin analysis by product, customer, salesman and branch
- Anomaly alerts for unusual discounts, voids, cash differences or sales drops
- Ask your data — type a question like "Which branch sold the most last Friday?" and get the answer with a chart
Connected to your systems
Analytics connect directly to your POS, ERP, accounting software or online store through secure data pipelines — no manual exports or spreadsheets.
FAQ
Frequently asked questions
What is AI analytics?
AI analytics uses machine learning on your business data to forecast demand, explain what drives your numbers and flag unusual activity — instead of only showing past totals.
How much history do we need?
Six to twelve months of sales data gives useful forecasts; more history improves seasonality such as Ramadan and Eid peaks.
Which systems can you connect?
Most POS, ERP and accounting systems, eCommerce stores, spreadsheets and databases.
Can we ask questions in plain language?
Yes. Type questions like “Which branch sold the most last Friday?” in English or Urdu and get the answer with a chart.
What kinds of anomalies can it detect?
Unusual discounts, voided sales, cash differences, sudden sales drops, stock mismatches and unexpected expense spikes.
How are insights delivered?
Through dashboards, scheduled email reports and WhatsApp summaries to the people you choose.
How accurate are the forecasts?
We measure forecast error on your own history before go-live and report it openly, then improve the models as more data arrives.
Do we need a data team?
No. We build and maintain the models and pipelines; your team just uses the insights.
Start with one process. Prove it in four weeks.
Pick the process that costs you the most time — we'll run a working pilot on your data.