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EXAMGURUXAI LABS

Service

Numbers that explain themselves.

Many teams have data but little time to read it. We clean and connect your data sources, build dashboards people actually use, and add forecasting and plain-language explanations of what changed. The aim is simple: fewer spreadsheet exports, earlier warnings and decisions based on evidence.

01What we build

What you get.

  1. 01

    Data pipelines

    Automated collection and cleaning from ERP, CRM, e-commerce, spreadsheets and databases into one reliable store.

  2. 02

    Management dashboards

    Focused dashboards for sales, operations and finance, designed around the decisions each team makes.

  3. 03

    Demand and sales forecasting

    Forecasts by product, region or branch, with ranges rather than false precision.

  4. 04

    Anomaly alerts

    Automatic alerts when a metric moves unusually, such as a sudden drop in orders or a spike in returns.

  5. 05

    AI-written summaries

    Weekly plain-language summaries of what changed and likely reasons, generated from the data and checked against it.

02Use cases

Where it pays back.

  1. 01

    Retail and e-commerce

    Forecast demand by SKU and store to reduce stock-outs and overstock.

  2. 02

    Hospitality

    Anticipate occupancy and staffing needs from bookings, seasonality and events.

  3. 03

    Logistics

    Predict volumes by lane or hub and spot delivery performance issues early.

  4. 04

    Finance and lending

    Track collections, cash flow and portfolio risk indicators in one view.

  5. 05

    Education businesses

    Understand enquiries, enrolments and retention across centres or programmes.

03Process

How we build it.

  1. 1

    Agree the questions

    We start from the decisions you need to make and the metrics that inform them.

  2. 2

    Connect and clean

    We connect sources, fix data quality issues and define metrics consistently.

  3. 3

    Build and forecast

    We build dashboards and forecasting models, and backtest forecasts against past periods.

  4. 4

    Adopt and refine

    We train users, schedule reports and refine the dashboards based on how teams use them.

Typical stack

  • Python / pandas
  • dbt
  • Postgres / BigQuery / Snowflake
  • Power BI
  • Metabase
  • Prophet / statsforecast
  • scikit-learn
  • Airflow

05FAQ

Common questions.

ask us anything else →

01Our data is messy. Can you still help?

Almost every project starts with messy data. The first phase focuses on connecting sources, fixing duplicates and definitions, and documenting what each metric means. We tell you early where data gaps limit what analysis or forecasting can reliably do.

02How accurate will the forecasts be?

That depends on your history, seasonality and how much outside events affect demand. We backtest models on past data so you can see realistic error ranges before relying on them, and we present forecasts as ranges rather than single numbers.

03Which BI tool should we use?

We work with Power BI, Metabase, Looker Studio and custom dashboards. If you already have a tool, we usually build on it. If not, we recommend one based on your budget, users and existing Microsoft or Google setup.

04Can AI explain changes in our numbers?

AI can draft plain-language summaries of what moved and suggest likely drivers based on your data. We ground those summaries in the underlying figures and treat them as a starting point for your team's judgement, not a final verdict.

Have a process worth automating?

Tell us about it — we'll reply with how data analytics & forecasting could handle it, what data it needs and a rough plan. The first conversation is free.