Aimstors

Data & Analytics: From scattered data to decisions you trust

Analytics is only useful when a number changes a decision. We start from the decisions you actually make weekly, then build the smallest data pipeline that answers them reliably — not a warehouse nobody opens.

custom · 3–8 weeks · 3 packages

Who this is for

  • Teams reconciling numbers across three tools every month
  • Founders who cannot state acquisition cost or retention confidently
  • Operations wanting forecasting instead of hindsight
  • Businesses sitting on years of unused transactional data

At a glance

  • First dashboard: 2–3 weeks
  • Sources: CRM, payments, ads, product events
  • Output: One metric definition per number
  • Reporting time: -90%
  • Single source: 1 warehouse
  • Forecast accuracy: ±7%

Define the questions first

We write down the ten questions the leadership team asks most often, and the action each answer would trigger. Anything that would not change a decision does not get a chart. This single step is what separates a dashboard people check daily from one that is bookmarked and forgotten.

Pipelines and a single source of truth

Data lands in one warehouse, transformations are versioned in code, and every metric has exactly one definition. When marketing and finance disagree about revenue, the fix is a definition, not another spreadsheet.

  • ETL from CRM, payments, ads and product events
  • Versioned transformations and a documented metric layer
  • Executive, sales and operations dashboards
  • Forecasting and anomaly alerts where the history supports it

Machine learning, honestly

Prediction is worth it when you have enough clean history and a decision that repeats. Churn scoring, demand forecasting and lead scoring usually qualify. If the data is thin, we say so and start with instrumentation — a model trained on noise is worse than no model.

What's included

  • Executive BI dashboards
  • Data warehousing and ETL
  • ML and forecasting pipelines
  • Real-time event analytics

What you receive

  • Source audit and metric dictionary
  • Warehouse with scheduled pipelines
  • Role-based dashboards
  • Automated reports to email / WhatsApp
  • Data quality monitoring

Our data & analytics process

  1. Map: Every source, owner and definition.
  2. Model: Warehouse schema and metric layer.
  3. Pipeline: Scheduled, monitored ingestion.
  4. Visualise: Dashboards per team and role.
  5. Automate: Alerts and recurring reports.

Technologies

Postgres, BigQuery, dbt, Metabase, Python, Airflow

Packages and pricing

  • Dashboard Pack (₹99,000): 3 dashboards on existing data. Best for: Quick visibility
  • Warehouse Build (₹2,50,000): ETL, warehouse, metric layer, dashboards. Best for: Multi-tool stacks
  • ML Program (Custom): Forecasting, scoring and model ops. Best for: Data-mature teams

Data & Analytics FAQs

We're on spreadsheets — too early?

No. That's the ideal moment to define metrics before bad habits harden.

Which BI tool?

We recommend based on your team, but Metabase and Looker Studio cover most needs.

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